smoking and its harms essay

Tobacco Smoking and Its Dangers Essay

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Introduction

The dangers of smoking, possible pro-tobacco arguments, annotated bibliography.

Tobacco use, including smoking, has become a universally recognized issue that endangers the health of the population of our entire planet through both active and second-hand smoking. Pro-tobacco arguments are next to non-existent, while its harm is well-documented and proven through past and contemporary studies (Jha et al., 2013). Despite this fact, smoking remains a widespread habit that involves about one billion smokers all over the world, even though lower-income countries are disproportionally affected (World Health Organization [WHO], 2016). In this essay, I will review the dangers of tobacco use and consider some of the remaining pro-tobacco arguments to demonstrate that no reason can explain or support the choice to smoke, which endangers the smoker and other people.

Almost every organ and system in the human body is negatively affected by tobacco, which is why smoking is reported to cause up to six million deaths on an annual basis (WHO, 2016, para. 2). The figure is expected to grow and increase by two million within the next fifteen years (Centers for Disease Control and Prevention [CDC], 2016a). Smoking can cause cancer in at least sixteen organs (including the respiratory and digestive systems), autoimmune diseases (including diabetes), numerous heart and blood problems (including stroke and hypertension); in addition, it damages lungs, vision, and bones, and leads to reproductive issues (including stillbirth) (U.S. Department of Health & Human Services, 2016).

Moreover, nicotine is addictive, and its withdrawal symptoms include anxiety, which tends to cumulate and contribute to stress (Parrott & Murphy, 2012). Other symptoms may involve mood swings and increased hunger, as well as thinking difficulties (Centers for Disease Control and Prevention [CDC], 2016b). Sufficient evidence also indicates that smoking is correlated with alcohol use and that it is capable of affecting one’s mental state to the point of heightening the risks of development of disorders (Cavazos-Rehg et al., 2014).

In the end, smoking reduces the human lifespan, as a result of which smokers are twice as likely as non-smokers to die between the ages of 25 and 79 (Jha et al., 2013, p. 341). Fortunately, smoking cessation tends to add up to ten years of life for former smokers, if they were to give up smoking before they turned 40 (Jha et al., 2013, p. 349). Similarly, the risk of developing mental issues also tends to be reversed to an extent, but it is not clear if it becomes completely eliminated or not (Cavazos-Rehg et al., 2014). The CDC (2016b) also reports that smoking cessation results in an improved respiratory condition and lower risks of developing cancer, cardiovascular diseases, and infertility.

At the same time, Cavazos-Rehg et al. (2014) state that there is not sufficient evidence to indicate that smoking cessation may cause mental issues, which implies that ceasing to smoke is likely to be a very good decision. Unfortunately, it is not always easy; many people make several attempts at quitting, experiencing difficulties because of abstinence symptoms, and in the process may gain weight and may require the help of professional doctors and counselors (CDC, 2016b). It is also noteworthy that only twenty-four countries in the world have comprehensive services aimed specifically at smoking cessation assistance (WHO, 2016, para. 18).

To sum up, tobacco is a drug that is harmful to people’s health, but it is also the basis of a gigantic industry that is subject to taxes, which implies that governments are typically interested in its development (CDC, 2016a). As a result, their spending in the field of prevention and cessation activities may not live up to expectations, despite the fact that governments have multiple means of reducing tobacco consumption, in particular, banning ads, adding taxes, and eliminating illicit trade (WHO, 2016). In the meantime, people who smoke search for arguments in order to rationalize their choice, which contributes to the deterioration of their own health and that of their communities.

It Is Not That Dangerous

It is admittedly difficult to find a reputable source that would promote smoking, which is understandable. However, certain pro-tobacco arguments can be suggested for the sake of attempting to understand the reasons for the phenomenon. For example, given the obvious lack of positive judgments, it may be hinted that the problem is overrated and the horrors of tobacco use are exaggerated. In this case, it is implied that scientific studies that highlight the dangers of smoking are not trustworthy to some extent. In fact, it cannot be denied that untrustworthy studies exist, but the scientific community does its best to eliminate them.

For example, the article by Moylan, Jacka, Pasco, and Berk (2012) contains a critique of 47 studies, which allows the authors to conclude that some research studies do not introduce sufficient controls. Despite this, the authors maintain that there is satisfactory evidence that indicates a correlation between certain mental disorders and smoking. They also admit that the evidence is less homogenous for some disorders, and suggest carrying out a further examination. As a result, it appears possible to consider the effects of tobacco use that are described by reputable organizations and peer-reviewed articles to be correct, which implies that all the horrible outcomes are indeed a possibility.

Tobacco Has Positive Effects

Given the information about tobacco’s negative effects, any number of positive ones that it may have appears insignificant. However, these may still be regarded as a pro-tobacco argument. One example is a calming, “feeling-good” effect that smokers tend to report. Parrott and Murphy (2012) explore this phenomenon, along with other mood-related effects of tobacco use, and explain that the feeling of calmness is the result of abstinence symptoms abatement.

In other words, smokers do not experience calmness when they get a cigarette; instead, they just stop experiencing abstinence-related anxiety. Moreover, apart from causing anxiety as an abstinence symptom, smoking tends to heighten the risks of various mental disorders, including anxiety disorder (Moylan et al., 2012), and alcohol use disorder (Cavazos-Rehg et al., 2014). It may be suggested that the belief in the positive effects of smoking is likely to result from the lack of education on the matter (WHO, 2016).

It Is My Free Choice

The freedom of choice is important to defend, and some may argue that they like the taste of the smoke or enjoy some of its effects (like the above-mentioned calming one), and they have the right to make a choice with regard to what they are going to do with their lives. Unfortunately, there is a factor that makes their choice more socially significant: Second-hand smoke intake also can affect people’s health in a negative way.

WHO (2016) estimates that about 600,000 non-smoking people, who never chose to smoke but were forced to inhale some second-hand smoke, die every year because of smoking-related issues (para. 2). In 2004, twenty-eight percent of second-hand smoke victims were children (WHO, 2016, para. 14). In other words, a smoker needs to be cautious and attempt to ensure that no deaths are caused by his or her free choice.

Moreover, even the freedom of the choice to smoke is sometimes questionable. In particular, the media has been accused of creating alluring images of smoking, which impairs the ability of people to make their own decisions (Malaspina, 2014). Similarly, the phenomenon of social smoking is explained by the wish to fit in within a community, to which teenage persons are especially prone (Nichter, 2015). As a result, the free choice argument may be regarded as typically invalid, which makes tobacco smoking even less reasonable or defensible.

It is extremely simple to argue against tobacco use: The activity has virtually no pluses, and any advantage that can be discovered by a diligent researcher would probably seem insignificant when contrasted to all the problems that smoking tends to cause. Despite this, people proceed to smoke as a result of the lack of education on the matter (WHO, 2016), harmful media images (Malaspina, 2014), and probably a number of other factors.

It is noteworthy, though, that since 2002, the number of people who have managed to quit smoking exceeds that of active smokers (CDC, 2016b, para. 22). Given the pressure of WHO (2016) in urging governments to do more to improve the situation, we may hope that tobacco use will be greatly reduced in the future, and people will stop engaging in this kind of self-harm.

Cavazos-Rehg, P. A., Breslau, N., Hatsukami, D., Krauss, M. J., Spitznagel, E. L., Grucza, R. A.,… & Bierut, L. J. (2014). Smoking cessation is associated with lower rates of mood/anxiety and alcohol use disorders . Psychological Medicine , 44 (12), 2523-2535. Web.

The article investigates the correlation between smoking cessation and certain mental disorders with the help of data from a national longitudinal study that was carried out in the United States between 2001 and 2006 by the National Institute on Alcohol Abuse and Alcoholism. The article concludes that there is a drop in anxiety disorder as well as the use of alcohol that is related to giving up smoking. The authors highlight the fact that the conclusion is not final and suggest that additional investigation is required. However, in their view, the idea that smoking cessation is related to an increased risk of anxiety disorders remains unproven and even contradicted by the results of their research.

For this essay, the article contributes information about the relationships between smoking and mood issues, which contradicts the myth about nicotine calming people. Also, it demonstrates the positive effects of giving up smoking, which is an argument against continued smoking.

Centers for Disease Control and Prevention. (2016a). Smoking & tobacco use . Web.

The website offers fast facts on tobacco use, including those for the world and the United States, and illustrates them with the help of statistics. The facts demonstrate that smoking has a negative impact on human health (limiting the lifespan and causing diseases) and results in significant costs for countries (primarily as healthcare expenditures). Also, the website mentions that tobacco prevention expenditures and efforts are often limited. The website finishes with statistics that illustrate the scope of the problem, that is, the number of smokers in the United States.

For this essay, the website contributes useful information and statistics on smoking and its consequences, including data on costs. Also, it mentions the profitability of the tobacco industry, and the issue of preventive measures, arguments that are capable of explaining the phenomenon of the continued existence of the problem of smoking.

Centers for Disease Control and Prevention. (2016b). Quitting smoking . Web.

The website contains information on the difficulties in quitting, provides relevant statistics, and suggests informative and supportive resources for those who wish to quit. It also highlights the dangers of smoking, the benefits of quitting, and the specifics of nicotine dependence.

For this essay, the website contributes some information on the dangers of smoking with a particular emphasis on the dependence and its consequences. The statistics can be used for illustrative purposes, in particular, with respect to quitting difficulties. However, the website also demonstrates that quitting is possible and beneficial, which is an argument against continued smoking that can be employed in the essay.

Jha, P., Ramasundarahettige, C., Landsman, V., Rostron, B., Thun, M., Anderson, R. N.,… & Peto, R. (2013). 21st-century hazards of smoking and benefits of cessation in the United States . New England Journal of Medicine , 368 (4), 341-350. Web.

The article is devoted to conducting a new research on life expectancy in smokers in order to take into account new factors of the changing environment. Also, the authors consider the life expectancy of the people who have quitted smoking. The study has an impressive sample size: It uses 202,248 histories of smoking cessation. The authors conclude that smokers’ lives are shorter while ceasing to smoke can help to “gain” several years, especially if it is done before the age of 40.

The article offers evidence on lifespan reduction as a result of smoking, and some data on smoking cessation benefits that can be used in the essay as arguments and illustrations. Also, the sample size of the article implies its credibility, making it a more attractive source.

Malaspina, A. (2014). False images, deadly promises . Broomall, Pa.: Mason Crest.

The book contains much information on smoking risks, but it focuses on the role of the media in popularizing this habit. Also, it considers other reasons for taking up smoking, including peer pressure, and mentions the problem of the profitability of the tobacco industry, which hinders the process of smoking eradication.

The book offers a comprehensive overview of the costs of tobacco, which makes it a very useful source. For the essay, the book contributes the study of media tobacco images, which is an interesting perspective. It can be used to demonstrate the question of free choice and the effect of the media on that choice.

Moylan, S., Jacka, F., Pasco, J., & Berk, M. (2012). Cigarette smoking, nicotine dependence and anxiety disorders: a systematic review of population-based, epidemiological studies . BMC Medicine , 10 (1), 123. Web.

The article reviews studies that are devoted to the correlation between anxiety and other mental disorders and smoking. The authors criticize some of the studies, demonstrating that there is limited evidence in some of them, but still conclude that the correlation between smoking and the risk of developing some disorders (in particular, generalized anxiety disorder) is sufficiently proven.

For the essay, the article provides direct information on tobacco use and its consequences and also demonstrates that unscrupulous studies are not unlikely to be produced, but this fact does not prove the lack of dangers in smoking. The existence of unscrupulous studies can be used as a pro-tobacco argument. Given the fact that it is difficult to find reputable sources that contain an alternative (approving) perspective on tobacco, it is a very important contribution to an argumentative essay.

Nichter, M. (2015). Lighting up . New York, NY: NYU Press.

The book contains a significant amount of information on tobacco-related issues, and it specifically focuses on the phenomenon of social smoking in college students. In particular, it discusses the issue of peer pressure as well as wrong perceptions, which are, in part, caused by the media. For example, it examines the harmful stereotype of smoking having a calming effect, which tends to attract youngsters who are experiencing a crisis.

The book is quite comprehensive and contains much useful information on smoking myths. For the essay, the book offers an explanation of one of the reasons for taking up smoking and demonstrates its harmfulness. It can be used to prove a pro-tobacco argument to be false and destructive.

Parrott, A. & Murphy, R. (2012). Explaining the stress-inducing effects of nicotine to cigarette smokers . Human Psychopharmacology: Clinical and Experimental , 27 (2), 150-155. Web.

The authors explain the mechanism of the abstinence symptoms in smokers, relate it to resulting anxiety disorders, and demonstrate that the perceived calming effect of smoking consists of addiction consequences. In other words, the authors demonstrate that tobacco is only capable of removing the abstinence-related anxiety caused by smoking tobacco, which makes the effect pointless. The authors also review prior studies and show that non-smokers or quitters are less likely to report irritability, stress, depression, and anxiety than smokers.

For the essay, the article explains one of the few pro-tobacco arguments (that smoking has a calming effect) and proves that it is false and harmful. As a result, the article is an important contribution that provides some information on the opposite point of view, according to which there are benefits to smoking, and proves it wrong.

U.S. Department of Health & Human Services. (2016). Effects of smoking on your health .

The website contains detailed information on health-related smoking effects. It demonstrates that there is hardly a part of a smoker’s body that remains unaffected. Also, the website describes particular issues that are caused by smoking, with respect to every specific part of a human body.

The website is the most comprehensive yet concise source in this bibliography with respect to smoking-related health issues. It presents information in the form of lists and pictures, which helps it to provide more details while taking up less space and readers’ time. For the essay, the website offers information on the health problems that are caused by smoking and describes them in greater detail than the rest of the sources.

World Health Organization. (2016). Tobacco fact sheet . Web.

The website offers limited statistics and information on the dangers of smoking and the process of quitting. Among other things, it describes the dangers of “second-hand” smoke with relevant statistics and an emphasis on the consequences for young children. Also, its states the WHO’s position on the matter, as well as the organization’s recommendations for government-level anti-tobacco activities.

For the essay, the website provides useful tobacco-related information that includes global statistics; the “second-hand” smoke information is also a very important argument that should be used in the paper. Moreover, the website creates a sense of urgency by demonstrating that the issue of tobacco smoking requires the attention of governments and healthcare organizations all over the world.

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IvyPanda. (2020, August 28). Tobacco Smoking and Its Dangers. https://ivypanda.com/essays/tobacco-smoking-and-its-dangers/

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Essay on Smoking

500 words essay on  smoking.

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

Ill-Effects of Smoking

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

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

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

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

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

How to Quit Smoking?

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

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

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

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

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

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

Conclusion of the Essay on Smoking

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

FAQ of Essay on Smoking

Question 1: What are the effects of smoking?

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

Question 2: Why should we avoid smoking?

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

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Essay on Harmful Effects of Smoking

Students are often asked to write an essay on Harmful Effects of Smoking in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Harmful Effects of Smoking

Introduction.

Smoking is a dangerous habit that poses significant health risks. It’s not only harmful to smokers, but also to those around them.

Health Risks

Smoking can cause lung cancer, heart disease, and stroke. It damages nearly every organ in the body, leading to premature death.

Secondhand Smoke

Non-smokers exposed to secondhand smoke face similar health risks. They can develop respiratory problems and increased risk of heart disease.

Impact on Environment

Cigarette butts litter the environment and release toxic chemicals into the soil and water, harming wildlife.

Smoking is harmful for everyone. It’s important to stay away from this deadly habit.

250 Words Essay on Harmful Effects of Smoking

Smoking is a widespread habit, yet it is one of the most detrimental practices to human health. Despite the awareness campaigns and statutory warnings, many continue to smoke, oblivious of the damaging effects it has on their health and wellbeing.

Physical Health Risks

Primarily, smoking causes numerous fatal diseases. It is the leading cause of lung cancer, accounting for about 85% of all cases. It also significantly increases the risk of heart diseases and stroke. The harmful chemicals in cigarettes damage blood vessels, leading to atherosclerosis, which can result in heart attack or stroke.

Impact on Respiratory System

Moreover, smoking adversely affects the respiratory system. It leads to chronic bronchitis, emphysema, and other lung diseases. The smoke and toxins inhaled damage the airways and alveoli, the tiny air sacs in the lungs, causing chronic obstructive pulmonary disease (COPD).

Effect on Mental Health

Smoking also influences mental health. Nicotine addiction can lead to increased stress, anxiety, and depression. The temporary relief from stress that smoking provides is often mistaken for a stress reliever, while it is actually exacerbating the problem.

In conclusion, smoking is a harmful habit that poses significant threats to physical and mental health. The myriad diseases it causes, coupled with its addictive nature, make it a dangerous lifestyle choice. It is imperative to raise awareness about these harmful effects and encourage cessation to safeguard public health.

500 Words Essay on Harmful Effects of Smoking

Smoking is a prevalent habit, often started out of curiosity, peer pressure, or stress management. However, its harmful effects are well-documented, impacting nearly every organ in the human body. Despite the widespread knowledge of its adverse effects, smoking continues to be a significant public health concern.

The Impact on Physical Health

One of the most severe consequences of smoking is its impact on physical health. Smokers are at a higher risk of developing a plethora of diseases, including lung cancer, heart disease, stroke, and chronic obstructive pulmonary disease (COPD). These conditions are often fatal, leading to premature death. The toxins in cigarette smoke damage the lining of the lungs, making smokers more susceptible to infections like pneumonia.

Detrimental Effects on Mental Health

Smoking doesn’t just harm the physical body; it also has a profound effect on mental health. Nicotine, the addictive substance in tobacco, alters the brain chemistry, leading to dependence. This dependence can exacerbate mental health conditions such as anxiety and depression. Furthermore, the stress of addiction and the struggle to quit smoking can also take a toll on mental well-being.

Smoking and Second-hand Smoke

The harmful effects of smoking are not confined to the smoker alone. Second-hand smoke, also known as passive smoking, is a significant concern. Non-smokers exposed to second-hand smoke inhale the same dangerous chemicals as smokers. This exposure increases their risk of developing heart disease, lung cancer, and other respiratory conditions.

Societal Impact

Smoking also has societal implications. The economic burden of smoking is substantial, with healthcare costs for smoking-related illnesses reaching astronomical levels. Additionally, the loss of productivity due to illness or premature death contributes to economic strain.

In conclusion, the harmful effects of smoking are far-reaching, affecting not only the smoker but also those around them and society at large. The physical and mental health implications, coupled with the economic burden, make it a significant public health issue. Despite the addictive nature of smoking, quitting is possible with the right support and resources, leading to improved health outcomes and quality of life. Understanding the full scope of smoking’s harmful effects is crucial in motivating smokers to quit and preventing non-smokers from starting.

That’s it! I hope the essay helped you.

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• Published: 10 October 2022

Health effects associated with smoking: a Burden of Proof study

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

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

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Matters Arising to this article was published on 14 April 2023

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

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

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

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

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

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

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

Five-star associations

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

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

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

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

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

Four-star associations

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

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

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

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

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

Three-star associations

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

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

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

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

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

Two-star associations

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

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

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

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

One-star associations

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

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

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

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

Age-specific dose–response risk for CVD outcomes

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

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

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

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

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

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

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

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

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

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

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

Selecting health outcomes

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

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

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

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

Selecting exposure categories

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

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

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

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

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

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

Step 6: estimating the mean risk function and the BPRF

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

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

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

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

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

Estimating the age-specific risk function for CVD outcomes

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

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

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

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

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

Estimating the theoretical minimum risk exposure level

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

Model validation

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

Statistical analysis

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

Statistics and reproducibility

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

Reporting summary

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

Data availability

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

Code availability

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

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Acknowledgements

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

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

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

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Correspondence to Xiaochen Dai .

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

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Extended data

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

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

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

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

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

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

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

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

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

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

Extended Data Fig. 6 Smoking and Breast Cancer.

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

Supplementary information

Supplementary information.

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

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

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Received : 11 April 2022

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

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Harms of Cigarette Smoking and Health Benefits of Quitting

What harmful chemicals does tobacco smoke contain.

Tobacco smoke contains many chemicals that are harmful to both smokers and nonsmokers. Breathing even a little tobacco smoke can be harmful ( 1 - 4 ).

Of the more than 7,000 chemicals in tobacco smoke, at least 250 are known to be harmful, including hydrogen cyanide , carbon monoxide , and ammonia ( 1 , 2 , 5 ).

Among the 250 known harmful chemicals in tobacco smoke, at least 69 can cause cancer. These cancer-causing chemicals include the following ( 1 , 2 , 5 ):

• Acetaldehyde
• Aromatic amines
• Beryllium (a toxic metal)
• 1,3–Butadiene (a hazardous gas)
• Cadmium (a toxic metal)
• Chromium (a metallic element)
• Ethylene oxide
• Formaldehyde
• Nickel (a metallic element)
• Polonium-210 (a radioactive chemical element)
• Polycyclic aromatic hydrocarbons (PAHs)
• Tobacco-specific nitrosamines
• Vinyl chloride

What are some of the health problems caused by cigarette smoking?

Smoking is the leading cause of premature, preventable death in this country. Cigarette smoking and exposure to tobacco smoke cause about 480,000 premature deaths each year in the United States ( 1 ). Of those premature deaths, about 36% are from cancer, 39% are from heart disease and stroke , and 24% are from lung disease ( 1 ). Mortality rates among smokers are about three times higher than among people who have never smoked ( 6 , 7 ).

Smoking harms nearly every bodily organ and organ system in the body and diminishes a person’s overall health. Smoking causes cancers of the lung, esophagus, larynx, mouth, throat, kidney, bladder, liver, pancreas, stomach, cervix, colon, and rectum, as well as acute myeloid leukemia ( 1 – 3 ).

Smoking also causes heart disease, stroke, aortic aneurysm (a balloon-like bulge in an artery in the chest), chronic obstructive pulmonary disease (COPD) ( chronic bronchitis and emphysema ), diabetes , osteoporosis , rheumatoid arthritis, age-related macular degeneration , and cataracts , and worsens asthma symptoms in adults. Smokers are at higher risk of developing pneumonia , tuberculosis , and other airway infections ( 1 – 3 ). In addition, smoking causes inflammation and impairs immune function ( 1 ).

Since the 1960s, a smoker’s risk of developing lung cancer or COPD has actually increased compared with nonsmokers, even though the number of cigarettes consumed per smoker has decreased ( 1 ). There have also been changes over time in the type of lung cancer smokers develop – a decline in squamous cell carcinomas but a dramatic increase in adenocarcinomas . Both of these shifts may be due to changes in cigarette design and composition, in how tobacco leaves are cured, and in how deeply smokers inhale cigarette smoke and the toxicants it contains ( 1 , 8 ).

Smoking makes it harder for a woman to get pregnant. A pregnant smoker is at higher risk of miscarriage, having an ectopic pregnancy , having her baby born too early and with an abnormally low birth weight, and having her baby born with a cleft lip and/or cleft palate ( 1 ). A woman who smokes during or after pregnancy increases her infant’s risk of death from Sudden Infant Death Syndrome (SIDS) ( 2 , 3 ). Men who smoke are at greater risk of erectile dysfunction ( 1 , 9 ).

The longer a smoker’s duration of smoking, the greater their likelihood of experiencing harm from smoking, including earlier death ( 7 ). But regardless of their age, smokers can substantially reduce their risk of disease, including cancer, by quitting.

What are the risks of tobacco smoke to nonsmokers?

Secondhand smoke (also called environmental tobacco smoke, involuntary smoking, and passive smoking) is the combination of “sidestream” smoke (the smoke given off by a burning tobacco product) and “mainstream” smoke (the smoke exhaled by a smoker) ( 4 , 5 , 10 , 11 ).

The U.S. Environmental Protection Agency, the U.S. National Toxicology Program, the U.S. Surgeon General, and the International Agency for Research on Cancer have classified secondhand smoke as a known human carcinogen (cancer-causing agent) ( 5 , 11 , 12 ). Inhaling secondhand smoke causes lung cancer in nonsmoking adults ( 1 , 2 , 4 ). Approximately 7,300 lung cancer deaths occur each year among adult nonsmokers in the United States as a result of exposure to secondhand smoke ( 1 ). The U.S. Surgeon General estimates that living with a smoker increases a nonsmoker’s chances of developing lung cancer by 20 to 30% ( 4 ).

Secondhand smoke causes disease and premature death in nonsmoking adults and children ( 2 , 4 ). Exposure to secondhand smoke irritates the airways and has immediate harmful effects on a person’s heart and blood vessels. It increases the risk of heart disease by an estimated 25 to 30% ( 4 ). In the United States, exposure to secondhand smoke is estimated to cause about 34,000 deaths from heart disease each year ( 1 ). Exposure to secondhand smoke also increases the risk of stroke by 20 to 30% ( 1 ). Pregnant women exposed to secondhand smoke are at increased risk of having a baby with a small reduction in birth weight ( 1 ).        

Children exposed to secondhand smoke are at an increased risk of SIDS, ear infections, colds, pneumonia, and bronchitis. Secondhand smoke exposure can also increase the frequency and severity of asthma symptoms among children who have asthma. Being exposed to secondhand smoke slows the growth of children’s lungs and can cause them to cough, wheeze, and feel breathless ( 2 , 4 ).

Is smoking addictive?

Smoking is highly addictive. Nicotine is the drug primarily responsible for a person’s addiction to tobacco products, including cigarettes. The addiction to cigarettes and other tobacco products that nicotine causes is similar to the addiction produced by using drugs such as heroin and cocaine ( 13 ). Nicotine is present naturally in the tobacco plant. But tobacco companies intentionally design cigarettes to have enough nicotine to create and sustain addiction. 

The amount of nicotine that gets into the body is determined by the way a person smokes a tobacco product and by the nicotine content and design of the product. Nicotine is absorbed into the bloodstream through the lining of the mouth and the lungs and travels to the brain in a matter of seconds. Taking more frequent and deeper puffs of tobacco smoke increases the amount of nicotine absorbed by the body.

Are other tobacco products, such as smokeless tobacco or pipe tobacco, harmful and addictive?

Yes. All forms of tobacco are harmful and addictive ( 4 , 11 ). There is no safe tobacco product.

In addition to cigarettes, other forms of tobacco include smokeless tobacco , cigars , pipes , hookahs (waterpipes), bidis , and kreteks . 

• Smokeless tobacco : Smokeless tobacco is a type of tobacco that is not burned. It includes chewing tobacco , oral tobacco, spit or spitting tobacco, dip, chew, snus, dissolvable tobacco, and snuff. Smokeless tobacco causes oral (mouth, tongue, cheek and gum), esophageal, and pancreatic cancers and may also cause gum and heart disease ( 11 , 14 ).
• Cigars : These include premium cigars, little filtered cigars (LFCs), and cigarillos. LFCs resemble cigarettes, but both LFCs and cigarillos may have added flavors to increase appeal to youth and young adults ( 15 , 16 ). Most cigars are composed primarily of a single type of tobacco (air-cured and fermented), and have a tobacco leaf wrapper. Studies have found that cigar smoke contains higher levels of toxic chemicals than cigarette smoke, although unlike cigarette smoke, cigar smoke is often not inhaled ( 11 ). Cigar smoking causes cancer of the oral cavity, larynx, esophagus, and lung. It may also cause cancer of the pancreas. Moreover, daily cigar smokers, particularly those who inhale, are at increased risk for developing heart disease and other types of lung disease.
• Pipes : In pipe smoking, the tobacco is placed in a bowl that is connected to a stem with a mouthpiece at the other end. The smoke is usually not inhaled. Pipe smoking causes lung cancer and increases the risk of cancers of the mouth, throat, larynx, and esophagus ( 11 , 17 , 18 ).
• Hookah or waterpipe (other names include argileh, ghelyoon, hubble bubble, shisha, boory, goza, and narghile): A hookah is a device used to smoke tobacco (often heavily flavored) by passing the smoke through a partially filled water bowl before being inhaled by the smoker. Although some people think hookah smoking is less harmful and addictive than cigarette smoking ( 19 ), research shows that hookah smoke is at least as toxic as cigarette smoke ( 20 – 22 ).
• Bidis : A bidi is a flavored cigarette made by rolling tobacco in a dried leaf from the tendu tree, which is native to India. Bidi use is associated with heart attacks and cancers of the mouth, throat, larynx, esophagus, and lung ( 11 , 23 ).
• Kreteks : A kretek is a cigarette made with a mixture of tobacco and cloves. Smoking kreteks is associated with lung cancer and other lung diseases ( 11 , 23 ).

Is it harmful to smoke just a few cigarettes a day?

There is no safe level of smoking. Smoking even just one cigarette per day over a lifetime can cause smoking-related cancers (lung, bladder, and pancreas) and premature death ( 24 , 25 ).

What are the immediate health benefits of quitting smoking?

The immediate health benefits of quitting smoking are substantial:

• Heart rate and blood pressure , which are abnormally high while smoking, begin to return to normal.
• Within a few hours, the level of carbon monoxide in the blood begins to decline. (Carbon monoxide reduces the blood’s ability to carry oxygen.)
• Within a few weeks, people who quit smoking have improved circulation, produce less phlegm , and don’t cough or wheeze as often.
• Within several months of quitting, people can expect substantial improvements in lung function ( 26 ).
• Within a few years of quitting, people will have lower risks of cancer, heart disease, and other chronic diseases than if they had continued to smoke.

What are the long-term health benefits of quitting smoking?

Quitting smoking reduces the risk of cancer and many other diseases, such as heart disease and COPD , caused by smoking.

Data from the U.S. National Health Interview Survey show that people who quit smoking, regardless of their age, are less likely to die from smoking-related illness than those who continue to smoke. Smokers who quit before age 40 reduce their chance of dying prematurely from smoking-related diseases by about 90%, and those who quit by age 45-54 reduce their chance of dying prematurely by about two-thirds ( 6 ).

Regardless of their age, people who quit smoking have substantial gains in life expectancy, compared with those who continue to smoke. Data from the U.S. National Health Interview Survey also show that those who quit between the ages of 25 and 34 years live about 10 years longer; those who quit between ages 35 and 44 live about 9 years longer; those who quit between ages 45 and 54 live about 6 years longer; and those who quit between ages 55 and 64 live about 4 years longer ( 6 ).

Also, a study that followed a large group of people age 70 and older ( 7 ) found that even smokers who quit smoking in their 60s had a lower risk of mortality during follow-up than smokers who continued smoking.

Does quitting smoking lower the risk of getting and dying from cancer?

Yes. Quitting smoking reduces the risk of developing and dying from cancer and other diseases caused by smoking. Although it is never too late to benefit from quitting, the benefit is greatest among those who quit at a younger age ( 3 ).

The risk of premature death and the chances of developing and dying from a smoking-related cancer depend on many factors, including the number of years a person has smoked, the number of cigarettes smoked per day, and the age at which the person began smoking.

Is it important for someone diagnosed with cancer to quit smoking?

Quitting smoking improves the prognosis of cancer patients. For patients with some cancers, quitting smoking at the time of diagnosis may reduce the risk of dying by 30% to 40% ( 1 ). For those having surgery, chemotherapy, or other treatments, quitting smoking helps improve the body’s ability to heal and respond to therapy ( 1 , 3 , 27 ). It also lowers the risk of pneumonia and respiratory failure ( 1 , 3 , 28 ). In addition, quitting smoking may lower the risk that the cancer will recur, that a second cancer will develop, or that the person will die from the cancer or other causes ( 27 , 29 – 32 ).

Where can I get help to quit smoking?

NCI and other agencies and organizations can help smokers quit:

• Visit Smokefree.gov for access to free information and resources, including Create My Quit Plan , smartphone apps , and text message programs
• Call the NCI Smoking Quitline at 1–877–44U–QUIT ( 1–877–448–7848 ) for individualized counseling, printed information, and referrals to other sources.
• See the NCI fact sheet Where To Get Help When You Decide To Quit Smoking .

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Essays About Smoking

Smoking essay, types of essay about smoking.

• Cause and Effect Essay: This type of essay focuses on the causes and effects of smoking. It discusses why people start smoking and the consequences of smoking on both the smoker and those around them.
• Argumentative Essay: This essay type aims to persuade the reader about the negative effects of smoking. It presents an argument and provides supporting evidence to convince the reader that smoking is harmful and should be avoided.
• Persuasive Essay: Similar to an argumentative essay, this type of essay aims to persuade the reader to quit smoking. It presents facts, statistics, and other relevant information to convince the reader to stop smoking.

Smoking Essay Example: Cause and Effect

• Identify the causes of smoking: Start by examining why people start smoking in the first place. Is it peer pressure, addiction, stress, or curiosity? Understanding the reasons why people smoke is crucial in creating an effective cause and effect essay.
• Discuss the effects of smoking: Highlight the impact smoking has on an individual's health and the environment. Discuss the risks associated with smoking, such as lung cancer, heart disease, and respiratory problems, and explain how smoking affects non-smokers through secondhand smoke.
• Use reliable sources: To make your essay more convincing, ensure that you use credible sources to back up your claims. Use scientific studies, government reports, and medical journals to support your arguments.
• Provide statistical evidence: Incorporate statistical data to make your essay more impactful. Use figures to show the number of people who smoke, the effects of smoking on the environment, and the costs associated with smoking.
• Offer solutions: Conclude your essay by suggesting solutions to the problem of smoking. Encourage smokers to quit by outlining the benefits of quitting smoking and offering resources for those who want to quit.

Smoking: Argumentative Essay

• Choose a clear position: The writer should choose a side on the issue of smoking, either for or against it, and be clear in presenting their stance.
• Gather evidence: Research and collect facts and statistics to support the writer's argument. They can find data from reliable sources like scientific journals, government reports, and reputable news organizations.
• Address counterarguments: A good argumentative essay will acknowledge opposing viewpoints and then provide a counterargument to refute them.
• Use persuasive language: The writer should use persuasive language to convince the reader of their position. This includes using rhetorical devices, such as ethos, pathos, and logos, to appeal to the reader's emotions and logic.
• Provide a clear conclusion: The writer should summarize the key points of their argument and reiterate their stance in the conclusion.

Persuasive Essay on Smoking

• Identify your audience and their beliefs about smoking.
• Present compelling evidence to support your argument, such as statistics, research studies, and personal anecdotes.
• Use emotional appeals, such as stories or images that show the negative impact of smoking.
• Address potential counterarguments and refute them effectively.
• Use strong and clear language to persuade the reader to take action.
• When choosing a topic for a smoking persuasive essay, consider a specific aspect of smoking that you would like to persuade the audience to act upon.

Hook Examples for Smoking Essays

Anecdotal hook.

Imagine a teenager taking their first puff of a cigarette, unaware of the lifelong addiction they're about to face. This scenario illustrates the pervasive issue of smoking among young people.

Question Hook

Is the pleasure derived from smoking worth the serious health risks it poses? Dive into the contentious debate over tobacco use and its consequences.

Quotation Hook

"Smoking is a habit that drains your money and kills you slowly, one puff after another." — Unknown. Explore the financial and health impacts of smoking in today's society.

Statistical or Factual Hook

Did you know that smoking is responsible for nearly 8 million deaths worldwide each year? Examine the alarming statistics and data associated with tobacco-related illnesses.

Definition Hook

What exactly is smoking, and what are the various forms it takes? Delve into the definitions of smoking, including cigarettes, cigars, pipes, and emerging alternatives like e-cigarettes.

Rhetorical Question Hook

Can we truly call ourselves a smoke-free generation when new nicotine delivery devices are enticing young people? Investigate the impact of vaping and e-cigarettes on the youth.

Historical Hook

Trace the history of smoking, from its ancient roots to its prevalence in different cultures and societies. Explore how perceptions of smoking have evolved over time.

Contrast Hook

Contrast the images of the suave, cigarette-smoking characters from classic films with the grim reality of tobacco-related diseases and addiction in the modern world.

Narrative Hook

Walk in the shoes of a lifelong smoker as they recount their journey from that first cigarette to a battle with addiction and the quest to quit. Their story reflects the struggles of many.

Shocking Statement Hook

Prepare to uncover the disturbing truth about smoking—how it not only harms the smoker but also affects non-smokers through secondhand smoke exposure. It's an issue that goes beyond personal choice.

Can Smoking Be Prevented by Making Tobacco Illegal

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Smoking Informative Speech

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The History of Tobacco Use and Its Dangers

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How Smoking Can Ruin Your Health

Fight addiction with the help willpower, should smoking be made illegal: argumentative, look of maturity: why smoking is "good" for you, nevada's smoking freedom at stake as joelle babula argues that local government should enforce strict laws, the effects of smoking ban, the challenges of quitting smoking, discussion on whether cigarette smoking should be banned in public places, the motif of smoking in all the pretty horses, the issue of smoking and alcohol drinking among adolescents, my personal experience of the effects of vaping, why vaping is bad for you: effects and dangers, feminist theory and communication, the toxic truth of smoking and vaping, the different harmful effects of smoking marijuana, pieces of advice that will help you to select the best vape shop in las vegas, facts of herbal cigarettes versus tobacco cigarettes, vaping: all you need to know about this trend, from cure to poison: the negative effects of tobacco, global efforts to diminish tobacco usage, relevant topics.

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Smoking and the Environment – How Smoking Harms the Planet

Smoking’s harms to users are well-known and widely-accepted, but smoking hurts more than just smokers. through deforestation, cigarette butt litter and air pollution, it harms the entire planet..

Tobacco use causes almost six million deaths per year, according to the CDC , and harms nearly every organ in the body. Thankfully, almost everybody is now aware of this, but the full scope of the harm caused by tobacco can’t be appreciated without considering the impact smoking has on the environment. This is a multi-faceted issue, with the link between smoking and the environment encompassing issues related to air pollution, the impacts of the growing of tobacco and the effect of the widespread littering of cigarette butts. If nothing else, this all goes to show that a tobacco-free world would be greener, too.

Smoking and Air Pollution

Second-hand smoke is widely recognized as a cause of disease in both humans and animals, but for the issue of smoking and the environment, the most important impact is how it affects air quality overall. Air pollution comes from a variety of sources, but some are more avoidable than others. For instance, pollution from vehicles is a significant issue for air quality around the world, but with so many people depending on cars, trucks and other pollution sources for their jobs and to transport materials they need, this is a hard issue to solve.

Smoking, however, is not necessary for the functioning of society, and in general is detrimental to it. Abundant evidence from various sources show marked improvements in air quality when smoking is banned. For example, when New York instituted a state-wide smoke-free law, levels of fine particulate matter in 20 locations studied decreased by 84%, and many other locations show similar results around the world.

The Impact of Tobacco Growing and Deforestation

Arguably the most important thing for the relationship between smoking and the environment is the impact of growing tobacco . Firstly, tobacco is grown as a mono-crop, and this means that large amounts of fertilizers, pesticides and herbicides are used when growing it. This can be hazardous to the environment, but the biggest issue is the risks to workers on tobacco farms. This can be mitigated with strong regulations, but tobacco is often grown in countries with very few controls to protect workers.

However, the biggest impact of smoking on the environment is deforestation. This occurs both to provide land for the growing of tobacco, but also to supply wood which is burned during the flue-curing process many tobaccos go through. In total, it’s estimated that 200,000 hectares of land are cleared annually to make room for tobacco cultivation.

The combined impacts of these two contributors to deforestation in countries like Tanzania make tobacco cultivation a particular issue. In Tanzania, four-fifths of the tobacco grown there is flue-cured, leading to a total of over 61,000 hectares of forested land being cleared for the purpose. The process of burning wood also releases CO 2 , so flue-curing directly contributes to global climate change.

Cigarette Butt Litter and the Environment

There is yet another issue related to smoking and the environment, and this relates to what happens to the remainders of cigarettes after they’re smoked. Cigarette butts are one of the most littered items throughout the world, with an estimated 4 trillion butts littered across the world each year.

Cigarette butts are not biodegradable , but they do break into smaller pieces under the influence of ultraviolet radiation from the sun. Cigarette butts also leach chemicals such as nicotine, arsenic, heavy metals and others like ethylphenol into the environment. As well as the direct effects on animals who ingest the cigarette butts, the chemicals released into the environment can indirectly damage animals, particularly marine animals. This is more serious because even cigarettes thrown onto the street far away from bodies of water can get swept into drains and find their way into the water system.

Reducing the Environmental Impacts of Tobacco

Smoking and the environment are intimately linked, and coming up with a way to solve the various problems should be a priority. The simplest solution is to reduce the number of smokers in society. This would reduce the demand for tobacco, which would eventually lead to less of it being grown, and also to less cigarette butts being littered.

However, there are other, more targeted strategies that can be used. For example, providing more places for smokers to dispose of their cigarette butts would reduce the issue of littering, and establishing rules governing the use of pesticides on tobacco farms could help exposed workers.

It’s important to remember, though, that the impacts of smoking on the environment are wide-ranging and hard to tackle. As challenging as it may be, the most reliable solution to the problem is to take steps to move towards a smoke-free world.

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Health Effects of Vaping

At a glance.

Learn more about the health effects of vaping.

• No tobacco products, including e-cigarettes, are safe.
• Most e-cigarettes contain nicotine, which is highly addictive and is a health danger for pregnant people, developing fetuses, and youth. 1
• Aerosol from e-cigarettes can also contain harmful and potentially harmful substances. These include cancer-causing chemicals and tiny particles that can be inhaled deep into lungs. 1
• E-cigarettes should not be used by youth, young adults, or people who are pregnant. E-cigarettes may have the potential to benefit adults who smoke and are not pregnant if used as a complete substitute for all smoked tobacco products. 2 3 4
• Scientists still have a lot to learn about the short- and long-term health effects of using e-cigarettes.

Most e-cigarettes, or vapes, contain nicotine, which has known adverse health effects. 1

• Nicotine is highly addictive. 1
• Nicotine is toxic to developing fetuses and is a health danger for pregnant people. 1
• Acute nicotine exposure can be toxic. Children and adults have been poisoned by swallowing, breathing, or absorbing vaping liquid through their skin or eyes. More than 80% of calls to U.S. poison control centers for e-cigarettes are for children less than 5 years old. 5

Nicotine poses unique dangers to youth because their brains are still developing.

• Nicotine can harm brain development which continues until about age 25. 1
• Youth can start showing signs of nicotine addiction quickly, sometimes before the start of regular or daily use. 1
• Using nicotine during adolescence can harm the parts of the brain that control attention, learning, mood, and impulse control. 1
• Adolescents who use nicotine may be at increased risk for future addiction to other drugs. 1 6
• Youth who vape may also be more likely to smoke cigarettes in the future. 7 8 9 10 11 12

Other potential harms of e-cigarettes

E-cigarette aerosol can contain substances that can be harmful or potentially harmful to the body. These include: 1

• Nicotine, a highly addictive chemical that can harm adolescent brain development
• Cancer-causing chemicals
• Heavy metals such as nickel, tin, and lead
• Tiny particles that can be inhaled deep into the lungs
• Volatile organic compounds
• Flavorings such as diacetyl, a chemical linked to a serious lung disease. Some flavorings used in e-cigarettes may be safe to eat but not to inhale because the lungs process substances differently than the gut.

E-cigarette aerosol generally contains fewer harmful chemicals than the deadly mix of 7,000 chemicals in smoke from cigarettes. 7 13 14 However, this does not make e-cigarettes safe. Scientists are still learning about the immediate and long-term health effects of using e-cigarettes.

Dual use refers to the use of both e-cigarettes and regular cigarettes. Dual use is not an effective way to safeguard health. It may result in greater exposure to toxins and worse respiratory health outcomes than using either product alone. 2 3 4 15

Some people who use e-cigarettes have experienced seizures. Most reports to the Food and Drug Administration (FDA ) have involved youth or young adults. 16 17

E-cigarettes can cause unintended injuries. Defective e-cigarette batteries have caused fires and explosions, some of which have resulted in serious injuries. Most explosions happened when the batteries were being charged.

Anyone can report health or safety issues with tobacco products, including e-cigarettes, through the FDA Safety Reporting Portal .

Health effects of vaping for pregnant people

The use of any tobacco product, including e-cigarettes, is not safe during pregnancy. 1 14 Scientists are still learning about the health effects of vaping on pregnancy and pregnancy outcomes. Here's what we know now:

• Most e-cigarettes, or vapes, contain nicotine—the addictive substance in cigarettes, cigars, and other tobacco products. 18
• Nicotine is a health danger for pregnant people and is toxic to developing fetuses. 1 14
• Nicotine can damage a fetus's developing brain and lungs. 13
• E-cigarette use during pregnancy has been associated with low birth weight and pre-term birth. 19 20

Nicotine addiction and withdrawal

Nicotine is the main addictive substance in tobacco products, including e-cigarettes. With repeated use, a person's brain gets used to having nicotine. This can make them think they need nicotine just to feel okay. This is part of nicotine addiction.

Signs of nicotine addiction include craving nicotine, being unable to stop using it, and developing a tolerance (needing to use more to feel the same). Nicotine addiction can also affect relationships with family and friends and performance in school, at work, or other activities.

When someone addicted to nicotine stops using it, their body and brain have to adjust. This can result in temporary symptoms of nicotine withdrawal which may include:

• Feeling irritable, jumpy, restless, or anxious
• Feeling sad or down
• Having trouble sleeping
• Having a hard time concentrating
• Feeling hungry
• Craving nicotine

Withdrawal symptoms fade over time as the brain gets used to not having nicotine.

Nicotine addiction and mental health

Nicotine addiction can harm mental health and be a source of stress. 21 22 23 24 More research is needed to understand the connection between vaping and mental health, but studies show people who quit smoking cigarettes experience: 25

• Lower levels of anxiety, depression, and stress
• Improved positive mood and quality of life

Mental health is a growing concern among youth. 26 27 Youth vaping and cigarette use are associated with mental health symptoms such as depression. 22 28

The most common reason middle and high school students give for currently using e-cigarettes is, "I am feeling anxious, stressed, or depressed." 29 Nicotine addiction or withdrawal can contribute to these feelings or make them worse. Youth may use tobacco products to relieve their symptoms, which can lead to a cycle of nicotine addiction.

• U.S. Department of Health and Human Services. E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General . Centers for Disease Control and Prevention; 2016. Accessed Feb 14, 2024.
• Goniewicz ML, Smith DM, Edwards KC, et al. Comparison of nicotine and toxicant exposure in users of electronic cigarettes and combustible cigarettes . JAMA Netw Open. 2018;1(8):e185937.
• Reddy KP, Schwamm E, Kalkhoran S, et al. Respiratory symptom incidence among people using electronic cigarettes, combustible tobacco, or both . Am J Respir Crit Care Med. 2021;204(2):231–234.
• Smith DM, Christensen C, van Bemmel D, et al. Exposure to nicotine and toxicants among dual users of tobacco cigarettes and e-cigarettes: Population Assessment of Tobacco and Health (PATH) Study, 2013-2014 . Nicotine Tob Res. 2021;23(5):790–797.
• Tashakkori NA, Rostron BL, Christensen CH, Cullen KA. Notes from the field: e-cigarette–associated cases reported to poison centers — United States, April 1, 2022–March 31, 2023 . MMWR Morb Mortal Wkly Rep. 2023;72:694–695.
• Yuan M, Cross SJ, Loughlin SE, Leslie FM. Nicotine and the adolescent brain . J Physiol. 2015;593(16):3397–3412.
• National Academies of Sciences, Engineering, and Medicine. Public Health Consequences of E-Cigarettes . The National Academies Press; 2018.
• Barrington-Trimis JL, Kong G, Leventhal AM, et al. E-cigarette use and subsequent smoking frequency among adolescents . Pediatrics. 2018;142(6):e20180486.
• Barrington-Trimis JL, Urman R, Berhane K, et al. E-cigarettes and future cigarette use . Pediatrics. 2016;138(1):e20160379.
• Bunnell RE, Agaku IT, Arrazola RA, et al. Intentions to smoke cigarettes among never-smoking US middle and high school electronic cigarette users: National Youth Tobacco Survey, 2011-2013 . Nicotine Tob Res. 2015;17(2):228–235.
• Soneji S, Barrington-Trimis JL, Wills TA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis . JAMA Pediatr. 2017;171(8):788–797.
• Sun R, Méndez D, Warner KE. Association of electronic cigarette use by U.S. adolescents with subsequent persistent cigarette smoking . JAMA Netw Open. 2023;6(3):e234885.
• U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease . Centers for Disease Control and Prevention; 2010. Accessed Feb 13, 2024.
• U.S. Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General . Centers for Disease Control and Prevention; 2014. Accessed Feb 12, 2024.
• Mukerjee R, Hirschtick JL, LZ Arciniega, et al. ENDS, cigarettes, and respiratory illness: longitudinal associations among U.S. youth . AJPM. Published online Dec 2023.
• Faulcon LM, Rudy S, Limpert J, Wang B, Murphy I. Adverse experience reports of seizures in youth and young adult electronic nicotine delivery systems users . J Adolesc Health . 2020;66(1):15–17.
• U.S. Food and Drug Administration. E-cigarette: Safety Communication - Related to Seizures Reported Following E-cigarette Use, Particularly in Youth and Young Adults . U.S. Department of Health and Human Services; 2019. Accessed Feb 14, 2024.
• Marynak KL, Gammon DG, Rogers T, et al. Sales of nicotine-containing electronic cigarette products: United States, 2015 . Am J Public Health . 2017;107(5):702-705.
• Regan AK, Bombard JM, O'Hegarty MM, Smith RA, Tong VT. Adverse birth outcomes associated with prepregnancy and prenatal electronic cigarette use . Obstet Gynecol. 2021;138(1):85–94.
• Regan AK, Pereira G. Patterns of combustible and electronic cigarette use during pregnancy and associated pregnancy outcomes . Sci Rep. 2021;11(1):13508.
• Kutlu MG, Parikh V, Gould TJ. Nicotine addiction and psychiatric disorders . Int Rev Neurobiol. 2015;124:171–208.
• Obisesan OH, Mirbolouk M, Osei AD, et al. Association between e-cigarette use and depression in the Behavioral Risk Factor Surveillance System, 2016-2017 . JAMA Netw Open. 2019;2(12):e1916800.
• Prochaska JJ, Das S, Young-Wolff KC. Smoking, mental illness, and public health . Annu Rev Public Health. 2017;38:165–185.
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• Lechner WV, Janssen T, Kahler CW, Audrain-McGovern J, Leventhal AM. Bi-directional associations of electronic and combustible cigarette use onset patterns with depressive symptoms in adolescents . Prev Med. 2017;96:73–78.
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Smoking and Tobacco Use

Commercial tobacco use is the leading cause of preventable disease, disability, and death in the United States.

For Everyone

Health care providers, public health.

The black market for cigarettes has exploded in plain sight and it's costing taxpayers billions

The sale takes place in plain sight.

At a convenience store on a Tuesday afternoon, the cashier asks: "Do you want the cheaper ones?"

"Sure," I say, thinking to myself: who wouldn't?

The cashier turns around, opens the black cupboard door with "Smoking Kills" scrawled across it and pulls out a packet of cigarettes.

He punches $25 into the EFTPOS machine and hands over the packet.

They are unquestionably illegal.

The tax alone on a packet of 20 cigarettes these days is $26.

But what's most surprising is, they look legitimate.

They're boxed in plain packaging, complete with a graphic image and the obligatory health warning — just like the ones you'd buy from a supermarket for two or three times the price.

If there was any doubt, it's the brand that gives it away: "Manchester Classic Gold" — possibly the most prolific illegal, and counterfeit, cigarette brand on the market, imported in vast quantities from the Middle East.

About a kilometre away, another convenience store is openly selling under-the-counter cigarettes (these ones from Japan, in their original packaging).

These transactions aren't taking place via encrypted messaging apps and the goods aren't being exchanged in brown paper bags in alleyways.

They're happening in hundreds of shops in towns and cities across the country and the evidence can be seen on any street.

On a chilly Canberra evening, two young guys smoking in a laneway complained to the ABC about the tobacco tax.

One of them pulls a packet of cigarettes out from his pocket — Marlboros in their original, trademark red packaging.

"Why would I buy the more expensive ones?" he questions.

This is a black market that has spiralled 'out of control', robbing taxpayers and legitimate retailers of billions of dollars.

And recently, it's taken a dark and dangerous turn.

In Victoria, organised crime has long been synonymous with the tobacco trade.

But last year, the underworld turf war exploded into public view.

Stores from Melbourne to Ballarat, selling illegal tobacco and vapes, have been torched by rival gangs vying for control of what's become a very lucrative black market. 

Tobacconists in Seville and Ballarat were targeted by arson attacks early this year.

In some cases, store owners are coerced into selling the illegal wares, warned by crime syndicates to "earn or burn".

Taskforce Lunar — set up by Victoria Police late last year — has so far arrested 62 people, including minors, as part of their investigations into nearly 60 arson attacks in the space of 12 months.

Victoria is undoubtedly at the epicentre of the tobacco wars but this is a nation-wide problem. 

Rohan Pike is a former Australian Federal Police (AFP) and Australian Border Force (ABF) officer who helped establish the original tobacco strike team, when the black market was, as he describes it, a "modest problem".

He says criminal syndicates and outlaw motorcycle gangs have had their claws in tobacco since the days when the crop was legally harvested in Victoria.

Back then, loose tobacco, or chop chop, would fall off the back of a truck and into the hands of smokers, tax-free.

To this day, police are still detecting — and destroying — illegal tobacco, often concealed among other crops like this one, worth $20 million, near Parkes in New South Wales.

The Illicit Tobacco Taskforce destroyed more than 264 tonnes of illicit tobacco since it was established in 2018.

But organised crime networks have become more sophisticated and as demand for cheap cigarettes has grown, they've adapted their operation from farming to importation.

"The number one driver of the problem is the enormous price of tobacco," Pike says bluntly.

"We're easily the most expensive country for tobacco in the world and it was natural that crime was going to follow."

Tipping point in war on smoking

Taking a look at the pack of Manchester Classic Gold from that Canberra convenience store, Pike concludes they're most likely from the United Arab Emirates and the counterfeit packaging is designed to deceive the ABF and police.

Tobacco is a dangerous product: it's one of the leading causes of preventable illness and death in Australia.

Around two in three people who smoke throughout their lifetime will die from their habit, according to a study published in BMC Medicine magazine.

Australia has "a lot to celebrate" when it comes to tobacco control, with an enviably low smoking rate of about 12 per cent of the adult population.

It's a testament to landmark plain packaging laws, advertising bans, restrictions on who can smoke and where, and a concerted, decades-long public health campaign.

But the cornerstone of tobacco control efforts has been tax.

At nearly $1.30 per cigarette, the excise alone on a packet of 25 cigarettes is $32, before you add GST. That's what smokers are avoiding when they buy under-the-counter products.

In the past decade, the excise has risen by 210 per cent, pushing the price of a packet of Winfield 25s from about $23 in 2014 to $47 today.

More popular brands, including Benson and Hedges, retail for around $65 for a pack of 25s.

Pike, who now works as an illicit trade advisor for Retail Trade Brand Advisory, believes the moment cigarettes hit the $50 mark was the tipping point. 

"Smokers began to view the price as extreme and even law-abiding people turned to the illicit market," Pike says.

Tax Office analysis estimates the size of the illicit market was at least $2.3 billion in 2021/22, around 13 per cent of the tobacco market. 

Pike believes it's grown to at least 25 per cent, an estimate backed by tobacco giant Philip Morris which told a parliamentary inquiry that one in four tobacco products consumed in Australia is illegal.

That is the demand organised criminals are exploiting.

As criminologists James Martin and David Bright from Deakin University explain, regulations can be used effectively to limit access to harmful products and reduce harm — that's the "sweet spot".

But as the history of prohibition has taught us, when restrictions become "onerous", they can create black markets.

"The violence unfolding on our streets suggests our current tobacco and vaping policies are failing to strike this balance," they wrote in The Conversation. 

Tax boon from tobacco 

The tobacco excise has undoubtedly helped reduce smoking rates but it's also grown to become a huge cash cow for successive federal governments.

Historically, the tax was increased twice yearly in line with inflation, but in 2010 – along with its plain-packaging reforms — the government started taking a more aggressive approach.

In that year, the tax was increased by 25 per cent, followed by annual hikes of 12.5 per cent between 2013 and 2020.

These hikes were in addition to the twice-yearly increases, which are now pegged to average earnings rather than inflation.

The Albanese government will increase the excise by a further five per cent each year for the next three years (on top of the twice-yearly increases) as part of its plan to drive smoking rates down to five per cent by 2030.

This will be accompanied by a ban on menthol cigarettes and fresh health warnings on both packets and cigarettes themselves. 

Health Minister Mark Butler reckons smoking rates have "flatlined" and a fresh crackdown is required.  

"I am not going to raise the white flag on smoking at 12 per cent of adults," Butler declared when announcing the excise hike.

Nicotine consumption has remained “largely steady” in recent years, according to the latest National Wastewater Monitoring Report.

However, the report – which cannot distinguish between cigarettes, nicotine patches and vapes – also warned of a “short-term” increasing trend in nicotine consumption that's been emerging since 2022.

Smoking rates are highest in regional areas, among First Nations Australians, low-income earners and people with a mental illness, meaning it's these groups bearing the brunt of the tax. 

Revenue from the excise peaked in 2019 when the government raked in $16.3 billion.

For context, this is more than the Commonwealth spends on the Child Care Subsidy or the JobSeeker unemployment benefit each year.

But it's been on a steep decline ever since and budget papers reveal the tax take has consistently fallen short of Treasury's forecasts.

This financial year, the tax was originally forecast to raise $15.3 billion.

In the budget, that's been slashed to $10.5 billion, which represents the lowest tax take since 2016.

Over the next five years, forecast revenue has been slashed by $12.5 billion — a "significant downward revision" — according to the budget papers, reflecting "weaker than expected tobacco imports … and consumption". 

If smoking rates have "flat-lined", as the health minister suggested, then these figures appear to back Pike's warning that Australians are not necessarily quitting smoking in big numbers — they're turning to illegal cigarettes and vapes.

Black market trade on fire

From the Australian Border Force headquarters in Canberra, Assistant Commissioner Erin Dale heads up the Illicit Tobacco Taskforce (ITTF), which includes officials from the ABF,  Tax Office, Australian Criminal Intelligence Commission and the AFP.

The numbers, Dale says, tell the story.

When the taskforce was established in 2018, more than 400 million cigarette sticks were detected and seized at the border. 

Last year, it was 1.7 billion.

That's a "400 per cent increase" in five years, says Dale.

"There's no secret there's been a huge increase in demand." 

Crime syndicates are flooding the border, importing illegal cigarettes in large and small quantities through international mail, air and sea cargo, and "mis-declaring" or "mis-identifying" containers in an attempt to evade authorities.

For them, it's considered a "low risk, high reward" crime because the penalties are far lower for importing illicit tobacco than they are for drugs like cocaine. 

The taskforce is constantly adapting and adjusting its approach, using intelligence to identify the supply chains, working with international counterparts to disrupt the trade in source countries and detecting anomalies in air and sea cargo to work out which containers to search.

It's a complex problem that Dale says cannot be solved by the taskforce alone. 

"Organised crime is looking to make profits from the demand that exists," Dale says. 

"By purchasing illicit tobacco, you're funding organised crime, and enabling organised crime to undertake other sinister activities."

After the interview, the cigarettes acquired by the ABC were handed over to be destroyed safely.

Stamping out black market 'like unscrambling an egg'

Because it's a federal tax, responsibility for cracking down on the black market has traditionally fallen to the federal government. 

It recently committed an extra $188 million dollars over four years to bolster the taskforce's efforts and to create a vaping and illegal cigarette commissioner — a position that's still yet to be filled.

But it's clear huge volumes of illegal cigarettes are still making it through the border, shifting the problem to the states where there's an "ad-hoc" approach to policing it.

Depending on the jurisdiction, a patchwork of local government, health department officials and police are responsible for enforcing the penalties for buying and selling illegal cigarettes.

In New South Wales and Victoria — the two most populous states — it's nearly impossible to monitor shops for compliance because there's no licensing regime for tobacco.

Retailers need to pay an annual licensing fee and pass a "fit and proper" test to sell lotto tickets and alcohol, but almost any shop can sell cigarettes. 

After dragging its feet for years, Victoria has promised to introduce a licensing regime by Christmas, no doubt spurred by the state's underground turf war.

It's a long overdue reform, according to Fred Harrison the CEO of Ritchies stores, a chain of independent retailers in Victoria.

His business has lost $120 million in legitimate tobacco sales in the past two years, he says, as the black market has "deteriorated alarmingly".

"Each year now for the last three years, the loss in legitimate sales has been greater and greater, to the point where illicit is now at its highest penetration," he says.

"People have turned to illicit, mainly, because it's so cheap."

But the costs to his business are even greater when you consider the fact that 62 per cent of customers who go to an IGA specifically to buy tobacco will also purchase two or three other items like milk and bread.

Harrison believes it's simply become "too easy" to buy illegal cigarettes and both the penalties imposed, and surveillance by authorities are "spasmodic and minimal."

"It's a little bit like trying to unscramble an egg," Harrison says.

Becky Freeman, an associate professor of public health at the University of Sydney, acknowledges the only reason people buy black market cigarettes is because "cigarettes are expensive".

But the answer, she says, lies in enforcing existing laws and controlling the supply, to make illegal cigarettes harder to come by.

"We can't just put our hands over our eyes and our fingers in your ears and pretend it doesn't exist," she says.

"But the solution is not to reduce the price. The solution is to manage the supply chain."

Freeman points to Australian Institute of Health and Welfare data which shows daily smoking rates have fallen sharply from 24 per cent of adults in 1991, to 11 per cent in 2019 and about 8.3 per cent in 2023.

Those rates are even lower among teenagers suggesting "we're really raising a smoke-free generation".

But alarmingly, that same generation is now turning to vapes or e-cigarettes, which have exploded in popularity among younger Australians.

The federal government is quickly tightening the screws on that market.

Anyone using a vape needs to obtain a prescription and in March, it became illegal to import vapes into Australia unless they're destined for a pharmacy, making it easier for Border Force to determine whether imports are legitimate.

The final piece of the government's vaping reforms is being debated in parliament to "close a loophole" and ban the sale of non-nicotine vapes in retail stores (many of which do in fact contain nicotine).

Australia is the only country in the world with a prescription model, and some fear this highly restrictive approach will see vapes go the same way as cigarettes.

Flavourhype Distribution owner Greg Isaacs — who has a vested interest in maintaining access to e-cigarettes — told a parliamentary inquiry that most of his customers had told him they would "most likely resort to the black market or just go back to smoking".

"Pharmacies are pricing these products to be more expensive than a pack of smokes," he says.

But these controls, Freeman says, will make enforcement far easier for authorities because, unlike cigarettes, only pharmacists will be able to legally sell vapes once the legislation passes.

At odds over cost

Freeman says a licensing regime for tobacco retailers, in every state and territory, is the first step towards disrupting the illicit tobacco trade. Eventually, she'd like to see the number of licenses capped.

Pike wants to see harsher penalties for buying and selling illegal cigarettes and believes enforcing these rules is a job for police, not health and local government officials. 

But he and Freeman strongly disagree on the question of pricing.

"The only real way to reverse that trend is to reduce the price of tobacco," Pike says, arguing it would push existing smokers back to legal cigarettes.

"But that is something that most politicians and policymakers just simply can't stomach."

As a public health expert, Freeman certainly can't stomach it. She says cutting the price of cigarettes would play into the hands of Big Tobacco.

"We know that price is a huge motivator for people to quit smoking," Freeman says, adding Australia's tax regime has set a "gold standard" globally.

In the meantime, smokers who know where to get cheap ciggies aren't likely to stop — why would they pay double for something they already know is bad for them?

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Health effects associated with smoking: a Burden of Proof study

Xiaochen dai.

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

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

Gabriela F. Gil

Marissa b. reitsma, noah s. ahmad, jason a. anderson, catherine bisignano, sinclair carr, rachel feldman, simon i. hay, vincent iannucci, hilary r. lawlor, matthew j. malloy, laurie b. marczak, susan a. mclaughlin, larissa morikawa, erin c. mullany, sneha i. nicholson, erin m. o’connell, chukwuma okereke, reed j. d. sorensen, joanna whisnant, aleksandr y. aravkin.

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

Christopher J. L. Murray

Emmanuela gakidou, associated data.

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

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

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

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

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

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

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

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

The increasing disease burden attributable to current smoking, particularly in low- and middle-income countries 4 , demonstrates the relevance of the present study, which quantifies the strength of the evidence using an objective, quantitative, comprehensive and comparative framework. Findings from the present study can be used to support policy makers in making informed smoking recommendations and regulations focusing on the associations for which the evidence is strongest (that is, the 4- and 5-star associations). However, associations with a lower star rating cannot be ignored, especially when the outcome has high prevalence or severity. A summary of the main findings, limitations and policy implications of the study is presented in Table ​ Table1 1 .

Policy summary

We evaluated the mean risk functions and the BPRFs for 36 health outcomes that are associated with current smoking 30 (Table ​ (Table2). 2 ). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 31 for each of our systematic reviews, we identified studies reporting relative risk (RR) of incidence or mortality from each of the 36 selected outcomes for smokers compared with nonsmokers. We reviewed 21,108 records, which were identified to have been published between 1 May 2018 and 31 May 2022; this represents the most recent time period since the last systematic review of the available evidence for the GBD at the time of publication. The meta-analyses reported in the present study for each of the 36 health outcomes are based on evidence from a total of 793 studies published between 1970 and 2022 (Extended Data Fig. ​ Fig.1 1 – 5 and Supplementary Information 1.5 show the PRISMA diagrams for each outcome). Only prospective cohort and case–control studies were included for estimating dose–response risk curves, but cross-sectional studies were also included for estimating the age pattern of smoking risk on cardiovascular and circulatory disease (CVD) outcomes. Details on each, including the study’s design, data sources, number of participants, length of follow-up, confounders adjusted for in the input data and bias covariates included in the dose–response risk model, can be found in Supplementary Information 2 and 3 . The theoretical minimum risk exposure level used for current smoking was never smoking or zero 30 .

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

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

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

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

Five-star associations

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

Results for all 5-star risk–outcome pairs are available in Table ​ Table2 2 and Supplementary Information 4.1 . In the present study, we provide detailed results for one example 5-star association: current smoking and lung cancer. We extracted 371 observations from 25 prospective cohort studies and 53 case–control studies across 25 locations (Supplementary Table 3 ) 5 , 6 , 32 – 107 . Exposure ranged from 1 pack-year to >112 pack-years, with the 85th percentile of exposure being 50.88 pack-years (Fig. ​ (Fig.1a 1a ).

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

We found a very strong and significant harmful relationship between pack-years of current smoking and the RR of lung cancer (Fig. ​ (Fig.1b). 1b ). The mean RR of lung cancer at 20 pack-years of smoking was 5.11 (95% uncertainty interval (UI) inclusive of between-study heterogeneity = 1.84–14.99). At 50.88 pack-years (85th percentile of exposure), the mean RR of lung cancer was 13.42 (2.63–74.59). See Table ​ Table2 2 for mean RRs at other exposure levels. The BPRF, which represents the most conservative interpretation of the evidence (Fig. ​ (Fig.1a), 1a ), suggests that smoking in the 15th–85th percentiles of exposure increases the risk of lung cancer by an average of 107%, yielding an ROS of 0.73.

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

Four-star associations

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

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

We found a strong and significant harmful relationship between pack-years of current smoking and RR of COPD (Fig. ​ (Fig.2b). 2b ). The mean RR of COPD at 20 pack-years was 3.17 (1.60–6.55; Table ​ Table2 2 reports RRs at other exposure levels). At the 85th percentile of exposure, the mean RR of COPD was 6.01 (2.08–18.58). The BPRF suggests that average smoking exposure raises the risk of COPD by an average of 72%, yielding an ROS of 0.54. The results for the other health outcomes that have an association with smoking rated as 4 stars are shown in Table ​ Table2 2 and Supplementary Information 4.2 .

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

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

Three-star associations

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

We present the findings on smoking and type 2 diabetes as an example of a 3-star risk association. We extracted 102 observations from 24 prospective cohort studies and 4 case–control studies across 15 locations (Supplementary Table 3 ) 117 – 144 . The exposure ranged from 1 cigarette to 60 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 26.25 cigarettes smoked per day.

We found a moderate and significant harmful relationship between cigarettes smoked per day and the RR of type 2 diabetes (Fig. ​ (Fig.3b). 3b ). The mean RR of type 2 diabetes at 20 cigarettes smoked per day was 1.49 (1.18–1.90; see Table ​ Table2 2 for other exposure levels). At the 85th percentile of exposure, the mean RR of type 2 diabetes was 1.54 (1.20–2.01). The BPRF suggests that average smoking exposure raises the risk of type 2 diabetes by an average of 16%, yielding an ROS of 0.15. See Table ​ Table2 2 and Supplementary Information 4.3 for results for the additional health outcomes with an association with smoking rated as 3 stars.

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

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

Two-star associations

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

We present the findings on smoking and breast cancer as an example of a 2-star association. We extracted 93 observations from 14 prospective cohort studies and 9 case–control studies across 14 locations (Supplementary Table 3 ) 84 , 87 , 145 – 165 . The exposure ranged from 1 cigarette to >76 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 34.10 cigarettes smoked per day.

We found a weak but significant relationship between pack-years of current smoking and RR of breast cancer (Extended Data Fig. ​ Fig.6). 6 ). The mean RR of breast cancer at 20 pack-years was 1.17 (1.04–1.31; Table ​ Table2 2 reports other exposure levels). The BPRF suggests that average smoking exposure raises the risk of breast cancer by an average of 4%, yielding an ROS of 0.04. See Table ​ Table2 2 and Supplementary Information 4.4 for results on the additional health outcomes for which the association with smoking has been categorized as 2 stars.

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

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

One-star associations

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

We use smoking and prostate cancer as examples of a 1-star association. We extracted 78 observations from 21 prospective cohort studies and 1 nested case–control study across 15 locations (Supplementary Table 3 ) 157 , 160 , 166 – 185 . The exposure among the exposed group ranged from 1 cigarette to 90 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 29.73 cigarettes smoked per day.

Based on our conservative interpretation of the data, we did not find a significant relationship between cigarettes smoked per day and the RR of prostate cancer (Fig. ​ (Fig.4B). 4B ). The exposure-averaged BPRF for prostate cancer was 0.94, which was opposite null from the full range of mean RRs, such as 1.16 (0.89–1.53) at 20 cigarettes smoked per day. The corresponding ROS was −0.06, which is consistent with no evidence of an association between smoking and increased risk of prostate cancer. See Table ​ Table2 2 and Supplementary Information 4.5 for results for the additional outcomes that have a 1-star association with smoking.

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

Age-specific dose–response risk for CVD outcomes

We produced age-specific dose–response risk curves for the five selected CVD outcomes ( Methods ). The ROS associated with each smoking–CVD pair was calculated based on the reference risk curve estimated using all risk data regardless of age information. Estimation of the BPRF, calculation of the associated ROS and star rating of the smoking–CVD pairs follow the same rules as the other non-CVD smoking–outcome pairs (Table ​ (Table1 1 and Supplementary Figs. 2 – 4 ). Once we had estimated the reference dose–response risk curve for each CVD outcome, we determined the age group of the reference risk curve. The reference age group is 55–59 years for all CVD outcomes, except for peripheral artery disease, the reference age group for which is 60–64 years. We then estimated the age pattern of smoking on all CVD outcomes (Supplementary Fig. 2 ) and calculated age attenuation factors of the risk for each age group by comparing the risk of each age group with that of the reference age group, using the estimated age pattern (Supplementary Fig. 3 ). Last, we applied the draws of age attenuation factors of each age group to the dose–response risk curve for the reference age group to produce the age group-specific dose–response risk curves for each CVD outcome (Supplementary Fig. 4 ).

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

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

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

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

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

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

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

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

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

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

Selecting health outcomes

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

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

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

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

Selecting exposure categories

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

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

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

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

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

In step 5, in addition to generating funnel plots and visually inspecting for asymmetry (Figs. ​ (Figs.1c, 1c , ​ ,2c, 2c , ​ ,3c 3c and ​ and4c 4c and Extended Data Fig. ​ Fig.6c) 6c ) to identify potential publication bias, we also statistically tested for potential publication or reporting bias using Egger’s regression 199 . We flagged potential publication bias in the data but did not correct for it, which is in line with the general literature 10 , 200 , 201 . Full details about the modeling process have been published elsewhere 29 and model specifications for each outcome are in Supplementary Table 6 .

Step 6: estimating the mean risk function and the BPRF

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

The BPRF 29 is a conservative estimate of risk function consistent with the available evidence, correcting for both between-study heterogeneity and systemic biases related to study characteristics. The BPRF is defined as either the 5th (if harmful) or 95th (if protective) quantile curve closest to the line of log(RR) of 0, which defines the null (Figs. ​ (Figs.1a, 1a , ​ ,2b, 2b , ​ ,3a 3a and ​ and4a). 4a ). The BPRF represents the smallest harmful (or protective) effect of smoking on the corresponding outcome at each level of exposure that is consistent with the available evidence. A BPRF opposite null from the mean risk function indicates that insufficient evidence is available to reject null, that is, that there may not be an association between risk and outcome. Likewise, the further the BPRF is from null on the same side of null as the mean risk function, the higher the magnitude and evidence for the relationship. The BPRF can be interpreted as indicating that, even accounting for between-study heterogeneity and its uncertainty, the log(RR) across the studied smoking range is at least as high as the BPRF (or at least as low as the BPRF for a protective risk).

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

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

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

Estimating the age-specific risk function for CVD outcomes

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

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

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

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

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

Estimating the theoretical minimum risk exposure level

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

Model validation

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

Statistical analysis

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

Statistics and reproducibility

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

Reporting summary

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

Online content

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

Supplementary information

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

Acknowledgements

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

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

Extended data

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

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

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

Author contributions

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

Peer review

Peer review information.

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

Data availability

Code availability, competing interests.

The authors declare no competing interests.

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

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

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

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World No Tobacco Day 2024 Theme: Date, History, Significance, Slogans, Messages, Posters And More

World No-Tobacco Day is an annual event observed worldwide to raise awareness about the dangers of using tobacco and to promote ways to control its use. According to the World Health Organization (WHO), tobacco use is a major cause of death and disease in India, leading to around 1.35 million deaths each year. This day aims to educate people about the harmful effects of tobacco on health, encourage smokers to quit and protect non-smokers from second-hand smoke. Here’s all you need to know about World No-Tobacco Day 2024 , including its date, theme, history, importance, slogans, messages, posters, and quotes.

World No-Tobacco Day 2024 Date

World No-Tobacco Day is celebrated every year on May 31st to raise awareness about the dangers of tobacco use and to advocate for effective policies to reduce its consumption. In 2024, World No-Tobacco Day will be observed on a Friday.

World No-Tobacco Day 2024 Theme

The theme for World No-Tobacco Day 2024 is “Protecting Children from Tobacco Industry Interference.” This theme highlights the need to stop the tobacco industry from targeting young people with harmful products and promotes policies that protect children from manipulative practices. The tobacco industry often targets youth through relaxed regulations, advertising tactics, and the rise of e-cigarettes . The aim is to educate people about these dangers and encourage governments to adopt policies that protect children from tobacco and related industries.

World No-Tobacco Day History and Significance

World No-Tobacco Day was started by the WHO in 1987 with a resolution called “World No Smoking Day” to encourage tobacco users to quit. The next year, May 31 was officially named World No-Tobacco Day, making it an annual global event.

In 1998, the WHO created the Tobacco-Free Initiative (TFI) to address the global health challenges caused by tobacco use. By 2008, the WHO used World No-Tobacco Day to call for a complete ban on all tobacco products and related advertising to fight the influence of tobacco companies targeting vulnerable people.

World No-Tobacco Day raises awareness about the health risks of tobacco use and advocates for strong measures to protect public health, especially among youth. The ultimate goal is to create a society free from the harmful impacts of tobacco. This yearly event, observed globally on May 31, provides a chance for individuals to quit tobacco use.

The Impact of World No-Tobacco Day

World No-Tobacco Day has a big impact on public health awareness and tobacco control policies worldwide. Each year, the day focuses on a specific theme to address current challenges and promote public health. The 2024 theme, “Protecting Children from Tobacco Industry Interference,” aims to highlight the aggressive marketing tactics used by tobacco companies to lure young people into tobacco use. By advocating for stronger regulations and protective measures, World No-Tobacco Day seeks to safeguard future generations from the dangers of tobacco.

World No-Tobacco Day Slogans and Campaigns

Every year, different slogans and campaigns are launched to promote World No-Tobacco Day. These slogans are powerful tools to convey the dangers of tobacco use and encourage people to take action. For World No-Tobacco Day 2024 , expect to see slogans focused on protecting children and advocating for stricter regulations on tobacco advertising and sales. These campaigns are designed to reach a wide audience and emphasize the message of a tobacco-free future.

World No Tobacco Day 2024 Messages and Quotes To Encourage Quitting Tobacco:

• "On this World No Tobacco Day, promise yourself to stop using tobacco and enjoy a healthier, happier life. Your future self will be grateful."
• “Smoking is hateful to the nose, harmful to the brain, and dangerous to the lungs." - King James
• "Sending you strength and determination on World No Tobacco Day. May you gather the courage to quit tobacco and begin a journey towards better health."
• "Today is a great day to make a positive change. Quit tobacco and take back your health. Happy World No Tobacco Day!"
• “Tobacco is the only industry that produces products to make huge profits and at the same time damage the health and kill their consumers." - Margaret Chan
• "Let this World No Tobacco Day encourage you to break free from tobacco's hold and embrace a life full of energy and happiness."
• "Cheers to a future without smoking! Wishing you the strength to quit tobacco and live a longer, healthier life. Happy World No Tobacco Day!"
• "It's better to hold a book between your fingers than to hold a cigarette." - Bista Nirooj
• "On World No Tobacco Day, remember that every effort to quit smoking is a step towards a brighter, healthier future."
• "Let today mark the beginning of a new, smoke-free phase in your life. Wishing you success in quitting tobacco on World No Tobacco Day."
• "May World No Tobacco Day be a turning point for you. Quit tobacco and open the door to endless possibilities and improved health."
• "Smoking kills. If you're killed, you've lost a very important part of your life." - Brooke Shields
• "Wishing you the best on World No Tobacco Day. Seize this chance to quit tobacco and give yourself the gift of good health."
• "Happy World No Tobacco Day! May you find the determination to quit tobacco and enjoy the numerous benefits of a smoke-free life."
• "Giving up smoking is the easiest thing in the world. I know because I’ve done it thousands of times" - Mark Twain

World No Tobacco Day Slogans

• "Protect Our Youth: Say No to Tobacco"
• "A Tobacco-Free Future Starts Today"
• "Kick the Habit, Save a Life"
• "Choose Health, Not Tobacco"
• "Stop Tobacco: Protect Future Generations"
• "Be Smart, Don't Start"
• "Tobacco Targets Youth: Let's Fight Back"
• "Breathe Easy, Live Free: Quit Tobacco"
• "Your Lungs Deserve Better: Say No to Tobacco"
• "Unmask the Truth: Tobacco Kills"

World No-Tobacco Day 2024 is an important event that highlights the ongoing fight against tobacco use and its harmful effects. By focusing on the theme “Protecting Children from Tobacco Industry Interference,” this year’s observance aims to shield young people from the manipulative tactics of the tobacco industry. As we approach May 31, let’s work together to spread awareness, support tobacco control measures, and strive for a healthier, tobacco-free world.

1. What is World No-Tobacco Day?

World No-Tobacco Day is an annual event observed globally on May 31st. It aims to raise awareness about the dangers of tobacco use and promote measures to control its consumption.

2. Why is World No-Tobacco Day important?

World No-Tobacco Day is important because tobacco use is a leading cause of death and disease worldwide. By educating people about the harmful effects of tobacco and advocating for tobacco control policies, the day contributes to public health efforts to reduce tobacco-related harm.

3. What is the theme for World No-Tobacco Day 2024?

The theme for World No-Tobacco Day 2024 is “Protecting Children from Tobacco Industry Interference.” This theme emphasizes the need to prevent the tobacco industry from targeting young people with harmful products and promotes policies to safeguard children from manipulative practices.

4. How can I participate in World No-Tobacco Day?

You can participate in World No-Tobacco Day by spreading awareness about the dangers of tobacco use, supporting tobacco control measures in your community, and encouraging smokers to quit. You can also join campaigns and events organized by public health organizations to mark the day.

5. What are some slogans for World No-Tobacco Day 2024?

Some slogans for World No-Tobacco Day 2024 may include:

• "Protect our future: Say no to tobacco!"
• "Tobacco targets kids – say no to industry interference!"
• "Be smoke-free, be healthy: Choose life over tobacco!"

6. How can I quit smoking or using tobacco products?

If you want to quit smoking or using tobacco products, there are various resources available to help you. You can consult a healthcare professional for advice and support, join a smoking cessation program, use nicotine replacement therapies, or seek counseling services. Remember, quitting tobacco is a journey, and support is available to help you succeed.

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The “Do no Harm” Oath: its Origins and Modern Relevance

This essay is about the “do no harm” oath, a fundamental principle in medical ethics derived from the Hippocratic Oath. It explores the historical origins of the principle and its application in modern medicine. The essay highlights how the “do no harm” concept guides physicians in weighing the benefits and risks of treatments, particularly in areas like pharmaceuticals and surgery. It also discusses the principle’s relevance in public health, medical research, and end-of-life care. Emphasizing the importance of patient safety, the essay underscores the ethical obligation of doctors to minimize harm while providing compassionate care.

How it works

The expression “do no harm” is frequently linked with the Hippocratic Oath, a foundational manuscript in the sphere of medicine. Albeit the precise verbiage “do no harm” does not overtly manifest in the original text, the essence is central to the moral practice of medicine. The principle is derived from the Latin phrase “primum non nocere,” signifying “first, do no harm.” This notion has been guiding healers for epochs, accentuating the significance of contemplating the potential injury of medical interventions and prioritizing patient safety.

Grasping the origins and relevance of this principle in modern medical practice furnishes insight into its enduring significance.

The Hippocratic Oath, ascribed to Hippocrates, an ancient Greek healer often hailed as the “Father of Medicine,” traces its roots to approximately the 5th century BCE. The authentic vow delineates a moral code for healers, centering on the duties of the healer to their patients and society at large. Fundamental components encompass confidentiality, non-maleficence (abstaining from causing harm), and beneficence (acting in the patient’s paramount interest). Although the precise wording of the oath has evolved over time, the essential principles have largely endured, attesting to the timeless nature of these moral directives.

In contemporary medicine, the principle of “do no harm” assumes heightened relevance. Medical advancements have ushered in increasingly intricate therapies and interventions, which, while often life-preserving, also harbor the risk of unintended repercussions. Healers must perpetually weigh the advantages of a particular treatment against its potential risks, ensuring alignment with the patient’s paramount interests. This delicate equilibrium constitutes a core facet of medical decision-making, underscoring the significance of the “do no harm” principle.

One domain where this principle is particularly discernible is in the realm of pharmaceutical interventions. The evolution of novel medications has revolutionized the management of numerous ailments, albeit introducing the prospect of adverse effects. Healers must meticulously weigh the potential injury associated with prescribing certain drugs, particularly when dealing with vulnerable demographics such as pediatric, geriatric, or multi-morbid patients. Stringent clinical trials and ongoing pharmacovigilance are imperative to uphold the “do no harm” principle in pharmacotherapy.

Surgical interventions furnish another exemplar of the application of “do no harm.” While surgery may be therapeutic or palliative, it entails inherent risks, encompassing infection, complications from anesthesia, and the possibility of postoperative sequelae. Surgeons must ensure that the anticipated benefits of the procedure outweigh these risks. Technological advancements and minimally invasive techniques have markedly diminished the likelihood of injury, yet the ethical imperative to prioritize patient safety remains paramount.

The principle of “do no harm” also extends beyond individual patient care to impinge upon public health initiatives and medical research endeavors. Public health policies necessitate a delicate equilibrium between safeguarding the populace and averting unintended injury. For instance, vaccination initiatives strive to forestall disease outbreaks while factoring in the rare adverse reactions that may ensue in select individuals. Similarly, medical research involving human subjects is governed by stringent ethical guidelines to ascertain that the prospective benefits outweigh the risks for participants.

Ethical quandaries frequently arise in scenarios where injury proves inevitable, compelling healers to elect the lesser of two evils. End-of-life care epitomizes such conundrums. Palliative care endeavors to assuage suffering and enhance the quality of life for terminally ill patients, yet interventions aimed at alleviating pain and distress occasionally precipitate hastened demise. Healers must navigate these intricate scenarios with empathy and discernment, upholding the tenet of minimizing injury while respecting patient autonomy and dignity.

The principle of “do no harm” also has implications for medical education and training. Aspiring healers are indoctrinated to approach patient care with humility, acknowledging the boundaries of their knowledge and proficiency. This mindset fosters a culture of continual learning and self-improvement, indispensable for minimizing injury and furnishing superlative care. Medical schools underscore the significance of empathy, communication, and ethical decision-making, equipping future healers to espouse the “do no harm” principle throughout their vocations.

In conclusion, the “do no harm” oath, rooted in the Hippocratic tradition, remains a linchpin of medical ethics. Its relevance spans diverse domains of medical practice, from individual patient care to public health initiatives and scientific inquiry. By prioritizing patient safety and meticulously evaluating the potential risks of interventions, healers can uphold this enduring principle. As medicine continues to evolve, the commitment to “do no harm” will persist as a guiding beacon for ethical and compassionate care.

It is imperative to bear in mind that this essay serves as a springboard for intellectual exploration and further inquiry. For bespoke guidance and assurance of adherence to academic standards, contemplation of seeking assistance from professionals at EduBirdie is advisable.

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Serge Schmemann

Editorial Board Member

Suddenly, a Real Chance to Halt the Fighting in Gaza

The “comprehensive new proposal” made by Israel for the Gaza war, announced by President Biden on Friday, is essentially a six-week cease-fire that would include the withdrawal of Israeli troops from populated areas of Gaza, the release of most Israeli hostages and a massive relief effort for two million battered, hungry Gazans. The stages beyond that — a permanent end to hostilities, release of all remaining hostages, the reconstruction of Gaza — are left to future negotiations.

That leaves a lot of open questions down the road, all heavily laden with polarized politics, animosities and unknowns. Yet if the plan Biden described on Friday is accepted by Hamas, which looks likely, the cease-fire alone would be a huge achievement for the United States and its mediating partners, Egypt and Qatar — and a desperately needed dollop of food, medicine, shelter and hope for Gazans.

Despite a deafening international outcry against the vast carnage and destruction in Gaza, including heated protests on American campuses and arrest warrants (albeit largely demonstrative) for top Israeli and Hamas leaders from the International Criminal Court, along with considerable pressure from the Biden administration, a cease-fire seemed always just beyond reach.

The reasons are many and varied: The Hamas terrorist attack of Oct. 7 left many Israelis hungry for the eradication of Hamas, cost be damned; Prime Minister Benjamin Netanyahu and his far-right nationalist government showed little interest in ending the fight, especially as that would most likely lead to the end of his fragile government and leave him facing criminal charges; the Hamas leader, Yahya Sinwar, insisted that the fighting must end before any hostage release or deal with Israel.

At the same time, the war put growing political pressures on Biden. There was always a threat of the conflict spreading to northern Israel and beyond, and the use of American ordnance against civilians in Gaza was feeding a swelling fury among American liberals, Biden’s constituency in a critical election year.

The president acknowledged some of the opposition the full proposal would confront in Israel. Responding to the longing for the destruction of Hamas, he claimed that the organization was no longer capable of an attack like the one on Oct. 7. Aware that some on the Israeli right wanted total victory, he argued that this would not bring hostages home, not bring an “enduring defeat” of Hamas and “not bring Israel lasting security.”

That is the message the president will have to relentlessly drive home over the six weeks the cease-fire is meant to last, if it starts and holds. All the obstacles to peace will still be in place as negotiations turn to a permanent end to hostilities. And Biden admitted that six weeks may not be enough. But for now, any respite is welcome.

Jesse Wegman

Fantasies Aside, Sentencing Trump Poses a Very Tough Choice

Donald Trump is not your average felon. That creates a devil of a dilemma for Juan Merchan, the New York trial judge who by July 11 must decide on an appropriate sentence for Trump after his conviction on 34 counts of falsifying business records. Send him to prison? Fine him, put him on probation, order him to perform 300 hours of community service?

It’s not a straightforward question. Unlike the federal system, New York has no formal sentencing guidelines, but decades of case law help judges weigh several familiar factors — including age, criminal record, and the severity of the offense — in determining the right punishment.

“So much depends on the individual,” Michael J. Obus, who sat on the New York State Supreme Court bench for 28 years, and alongside Merchan for more than a decade, told me. “There’s just no precedent for this particular individual that makes this an easy decision. Everything pulls you in different directions. On the one hand, you’ve got a man who’s 77 years old and is convicted of the lowest-level, Class E felony. On the other hand, he’s been held in contempt 10 times and is not showing any remorse.”

As I followed the trial over the last several weeks, and then absorbed the jury’s verdict on Thursday evening, I found myself torn. Trump is a uniquely malign, and uniquely powerful, figure in American life. He mocks the notion of equal justice even as he squeals endlessly about his own mistreatment. He considers himself above the law even as he threatens to wield it against his enemies if given the chance. He poses an extreme danger to the rule of law and the future of democracy that no workaday carjacker could dream of.

And yet if no one is above the law, no one is below it either. Trump can’t be given an unusually strong sentence simply for being a bad president, or an immoral lout, or for any other reason not directly related to the specific crimes in this case and the usual factors judges consider.

Orange-jump-suited liberal fantasies aside, most people convicted of low-level, nonviolent felonies in New York are not sentenced to prison . At the same time, Obus pointed out, several of Trump’s crimes “took place while the defendant was in the White House. You can’t say that about most defendants.” (Trump may yet talk his way into the lockup if he keeps testing the limits of Merchan’s gag order.)

I don’t know the right answer and can’t predict Merchan’s decision. What I do know is that in a healthy country, a nominee for president would not come anywhere near the line of criminality — certainly not so close that reasonable people can debate whether he should spend time behind bars.

This is the situation we are faced with. That this candidate may yet prevail in November is the biggest predicament of all.

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Jonathan Alter

Contributing Opinion Writer

Six of Trump’s Dumbest Trial Mistakes

In the avalanche of post-conviction coverage, we’ve all heard that if Donald Trump had just copped to a misdemeanor, or admitted to having sex with Stormy Daniels, or been allowed to have his expert witness testify fully, he wouldn’t be a felon.

Maybe so, but that’s beside the point. Trump could still have been Trumpy and possibly won the case if he and his lawyers had not made six critical mistakes:

Trump disrespected the judge. The last thing any defendant should do is tick off the judge. Trump and his team did so repeatedly. His 10 citations for contempt of court, which almost got him thrown in jail, were just the start of it. Trump’s lawyers made a series of frivolous and repetitive motions that did their case — and their own reputations — no good. The only explanation is that their volcanic client must have insisted on it.

Trump dozed for much of the trial. Jurors might have been fine with him falling asleep occasionally. But inside the courtroom we noticed on the monitors (which showed Trump from the front) that his eyes were closed every day and for large portions of the testimony. That’s a bad look for any defendant trying to win favor with the jury.

The defense blew its cross-examination of Stormy Daniels. Susan Necheles, one of the defense lawyers, had a tough assignment: Prove Stormy Daniels was lying about her sexual encounter with Trump when she clearly was not. But Necheles made it worse by seeming to shame Daniels for her career choices, falsely hinting she had records to disprove the account, and by failing to object more when Daniels got prurient, as the judge later pointed out.

The defense dropped its best anti-Michael Cohen argument. The lead defense attorney, Todd Blanche, elicited on cross-examination that Cohen thought William H. Pauley, a former federal judge, was in on some kind of crazy conspiracy with Cohen’s enemies to hurt him. But Blanche was so eager to catalog all of Cohen’s lies that he failed to focus on Cohen’s wildest charge later.

The defense insisted on putting on a case. If the defense had rested without calling witnesses, Trump’s refusal to testify would have made a certain sense, suggesting the prosecution’s case was so weak there was no need to rebut it. Instead, the defense called the thuggish Robert Costello, who was by far the worst, least credible witness in the entire trial and was destroyed on cross-examination. This ended the trial on a down note for the defense.

The defense lied about the “Access Hollywood” tape. “You heard that politicians reacted negatively to the ‘Access Hollywood’ tape,” Blanche said, in one of the biggest lies during his closing. “None of that is true.” Oh, yeah? So why was Trump nearly dumped from the 2016 ticket over it? This jury wasn’t born yesterday, and they had heard lots of testimony about the effect of that explosive tape.

Trump still doesn’t get that a court of law is not the same as the court of public opinion. Politicizing the trial might be good for donations and even polling but it will hurt him on July 11, when he will have to answer for his antics at sentencing.

Zeynep Tufekci

Opinion Columnist

Bird Flu Doesn’t Have to Become History’s Most Avoidable Disaster

The Michigan Department of Health and Human Services reported on Thursday that another farmworker has been infected with H5N1, an avian flu virus. Alarmingly, unlike earlier cases, he has respiratory symptoms. This means the virus is in his lungs, where it has a better chance to evolve into an airborne form that could easily infect others.

Viruses often hit a dead end when they cross from one species to another, getting stuck at their first victim. For example, H5N1 has been around since the 1990s, but most patients have had extensive, direct contact with sick poultry and almost never pass it on to other humans.

The pathogens that have the greatest potential to set off a pandemic often have a deadly combination of airborne transmission and frequent mild cases, allowing them to spread widely and stealthily. That’s a key reason there hasn’t yet been an Ebola pandemic. The disease causes severe illness and kills most victims, and it mainly spreads through close contact with infected bodily fluids. It has fewer chances to spread widely than another disease might.

The United States is certainly giving H5N1 many, many chances to adapt to spreading easily and quietly among humans.

Cows started getting sick with H5N1 last winter, but unlike birds with H5N1, they weren’t dying. It took dogged investigation by Dr. Barb Petersen, a veterinarian in Texas, to determine that they were afflicted with a form of avian influenza. When we spoke, she told me that whenever cows fell sick on farms she monitored, an unusual number of people also became ill.

In April, the Centers for Disease Control and Prevention reported a farmworker in Texas had been infected with H5N1 . This month, state health officials in Michigan found two more human cases (including the one reported on Thursday). Even so, public health officials have largely been slow to establish the sort of widespread testing and data sharing that would give Americans the best chance at stopping an H5N1 pandemic.

This month, Dr. Mandy Cohen, the director of the C.D.C., told The New York Times there were no immediate plans to make testing mandatory. But if we don’t test for H5N1, we won’t find it.

As Rick Bright, an expert on the H5N1 virus who served on President Biden’s coronavirus advisory board, told me : “We are missing additional cases by not testing. We are missing evolutionary patterns of the virus by not sequencing more. We are also losing the trust of people by not being completely timely and transparent with data and information as it becomes available.”

This virus may never evolve to spread dangerously among humans, but if it does, this particular avian flu pandemic will go down as one of the most avoidable slow-motion disasters in history.

David Firestone

Deputy Editor, the Editorial Board

Trump Is ‘Honored’ by a Verdict That Should Shame Him

Donald Trump could have gone any number of directions on Friday morning in his first speech after becoming a felon. A better human being might have expressed some remorse or a modicum of respect for the jury’s verdict, but that’s not who he is. He might have at least acknowledged that the historic conviction was a significant defeat and urged his supporters to help him rise above it in pursuit of some larger goal. But he didn’t even do that.

In fact, what was remarkable about the speech at Trump Tower was how little effect the conviction seems to have had on his permanent vocabulary of grievance. To hear him tell it, it’s just another speed bump erected by what he called the “fascists” in the Democratic Party, no different from his two impeachments, or the devastating Jan. 6 investigation, or the judgments against his business, or the civil finding that he is a sexual abuser. To acknowledge that this moment is spectacularly different would be to give the verdict real power, and he was determined to rob the legal system of any ability to get in his way.

And so, even though he must have known that the audience for this speech would be unusually large, he tossed out the same jumbled shards of anger, lies and hyperbole that he dispenses every day on the trail. The usual journalistic cliché for speeches like this one is “rambling,” but at least on a ramble you actually go someplace, if slowly. Trump, on the other hand, had no apparent destination.

He insulted the district attorney, Alvin Bragg, and said Justice Juan Merchan “looks like an angel but he’s really a devil.” But those attacks were blended with fevered denunciations of President Biden for imaginary policies like banning cars, letting vast mobs of terrorists march unimpeded into the country and ruining the country with a politicized system of justice. “We are living in a fascist state,” he said.

Regarding the trial itself, he bashed Michael Cohen as a “sleazebag,” denied having a sexual affair and dismissed the crime of falsifying business records as some kind of bookkeeping hiccup. The jury didn’t believe any of this for a minute, of course, but Trump will never stop trying to litigate it, and he was even less effective than his inept lawyers.

The only real acknowledgment that something unusual had happened was when he called the conviction “a great, great honor” that he was willing to bear, as if he were Saint Sebastian, pierced by Democratic arrows for the country’s greater good. His wounds, however, are entirely self-inflicted.

Kathleen Kingsbury

Opinion Editor

What the Nation Needs to Hear From Trump (but Won’t)

Donald Trump has announced he plans to speak to the nation from Trump Tower on Friday morning. It made me pause and consider what I — and I suspect many voters, especially swing voters, and maybe even some of Trump’s supporters — want to hear less than 24 hours after the first conviction on felony charges of a former president.

What I want to hear — from a sober, humbled presidential candidate — is this: “Yesterday a jury of my peers rendered a verdict against me and my actions in 2016. I have believed, from the get-go, that this prosecution was politically motivated, and there is evidence that the district attorney always intended to bring it, despite a lack of any good evidence that I committed a crime. I continue to think the case rested on a bogus legal theory, and we will appeal. But until that appeal is ruled on, I will respect this verdict, as much as I disagree with it.”

In other words, I would like the former, and potentially future, president to rise to the seriousness of the occasion.

Of course, Trump will say no such words, and he will express no remorse in a way that might lighten his eventual sentence. He will declare the trial rigged, and he will rail against the judge, the court and the jury, despite the responsible and somber ways in which all parties conducted themselves. He will ask his supporters to join him in his outrage, and he will continue — as he has done time and time again — to undermine the law and democratic norms.

He will not rise to the occasion. And our country will be worse off for it.

‘Is That Your Verdict?’ As Trump Seethes, a Jury Says ‘Yes.’

The verdict might not have been a total surprise, but the timing sure was.

All Thursday afternoon, those of us in the courtroom watching the Donald Trump trial had been expecting a Friday verdict. This was validated when, a little before 4 p.m., Justice Juan Merchan came into the courtroom and told us that the jury would be excused at 4:30 and would resume deliberations on Friday.

Then crickets. For more than half an hour, we heard nothing — certainly not the buzzy bell we expected if the jury had a note to send or a verdict.

The judge had left the bench to tell jurors he was excusing them and hadn’t come back.

I had a nice whispered chat with Andrew Giuliani, a fervent Trump supporter who was sitting behind me. I told him I expected a conviction and asked him if he would blame his fellow New Yorkers who had spent many hours painstakingly examining the evidence. He said yes and took a shot at Matthew Colangelo, the federal prosecutor who came in from the Justice Department to help the D.A.’s office.

“That’s totally unprecedented!” Giuliani said, previewing some of the team’s damage-control spin. I reminded him that this had been done countless times in civil rights cases in the South and complex local prosecutions.

Around 4:30 p.m., Merchan mounted the bench and announced that he had received a note from the jury. I first thought it was another request for more evidence to be read back. This was a conscientious jury that had been deliberating since midday on Wednesday. But the note said that a verdict had been reached and jurors needed another half-hour before announcing it.

You could hear a collective gasp in the courtroom.

At 5:03 p.m., the jury entered. After the foreman, an Irish-born former waiter clad in a blue pullover, stood and confirmed that the jury had reached a verdict, he was asked about each count and said “guilty” 34 times.

The defense was asked if it wanted to waive its right to poll the jurors and, of course, said no. When asked, “Is that your verdict?” each of the other 11 jurors — their poker faces intact — calmly answered, “Yes.”

Trump had become a felon.

Merchan thanked the jurors for their service in a “very stressful and difficult task” and told them they are “free to discuss the case, but you are also free not to. The choice is yours.”

Then came what I have always viewed as the most moving part of the trial, a ritual of democracy performed eight times a day, as the jury moves back and forth for breaks and lunch and exits when court adjourns:

The jurors marched past Donald Trump without looking at him, soldiers for justice.

Trump’s lead attorney, Todd Blanche, moved for a “judgment of acquittal” because there’s “no way this jury could have reached a verdict without accepting the testimony of Michael Cohen.”

Merchan thought he heard Blanche say that even the judge knew Cohen had perjured himself on the stand. Blanche backtracked, and the motion was denied with dispatch.

At the request of Blanche, who has other Trump legal proceedings to deal with in June, Merchan set sentencing for July 11. It struck me that since Trump is guilty of 34 felonies in the first degree, he is unlikely to get off with a slap on the wrist. First he will have to undergo a probation interview, followed by a probation report.

This summer, will we be discussing ankle bracelets in the White House? Quite possibly.

Merchan asked for Trump’s current bail status.

In what may have been my favorite line of the day after “guilty,” the prosecution lawyer Joshua Steinglass said, “No bail, judge.”

In another trial, that might mean the felon had been denied bail. Here it was a simple recognition of the stark reality that a jury had just convicted a former president of the United States, who would not be sent to a holding cell.

As usual, Trump walked up the center aisle, swinging his right arm out in an exaggerated handshake with his son Eric. He looked more hunched than usual, with pain on his face.

In the elevator a photographer who has been shooting him for years said, “I have never seen him looking so tired.”

Trump’s Trial and Lincoln’s Example Make 2024 a Character Test

History hangs heavily over the Manhattan Criminal Courthouse this week. Everyone inside feels its weight. After the jury was sent to deliberate, things loosened up a bit and I chatted with a court police officer from Brooklyn who for weeks has been one of our hall monitors. She spoke of being able to someday tell her grandchildren that she witnessed a historic trial.

There has been other presidential history made in Lower Manhattan, of course. In 1860, little more than three months before Abraham Lincoln became the Republican nominee for president, he came to New York, where he bought a new suit at Brooks Brothers and a stovepipe hat. He also stopped by a home for desperately poor children, just two blocks south of where the courthouse now stands. The children’s faces, a witness reported, “would brighten into sunshine as he spoke cheerful words of promise.” When told he had inspired the children, Lincoln responded, “No, they inspired me.”

Lincoln’s address during that New York trip at the Cooper Union, a mile north of the current courthouse, would help catapult him to the presidency. He wrote the ending in all capital letters:

LET US HAVE FAITH THAT RIGHT MAKES MIGHT, AND IN THAT FAITH LET US, TO THE END, DARE TO DO OUR DUTY AS WE UNDERSTAND IT.

Donald Trump often compares himself to Lincoln, noting that they are both honest and Republicans. But he has inverted Lincoln’s motto of right making might, believing the opposite. He also castigated Lincoln for nearly losing to a bigger hero, the Confederate general Robert E. Lee. And the ultimate un-Lincoln constantly celebrates Jan. 6 insurrectionists who threatened to kill his vice president and flew a Confederate flag under the Capitol dome built by Lincoln.

The liar and cheat we’ve heard the most about in the courtroom for the past six weeks is not Michael Cohen but Trump, whose basic values deviate not only from Lincoln’s but also from those of any man who has ever held the office of president.

I understand why many voters might favor Trump under the mistaken impression that he has their back. But for any leaders or well-educated people in any realm — from Nikki Haley to the billionaire Stephen Schwarzman to your otherwise intelligent uncle — to support Trump now out of narrow self-interest raises deeper questions about their patriotism.

They know better, and as the evidence presented in this trial has shown, they are failing the character test of their generation.

Now we are engaged in our own cold civil war, and American voters must think harder about whether this nation — or any nation — can long endure the consequences of another Trump presidency.

Adam Sternbergh

Opinion Culture Editor

Ángel Hernández Made Baseball Great

The retirement of Ángel Hernández, long reputed to be the worst umpire in baseball, was greeted by many baseball fans with unfettered glee . In an age when the strike zone is constantly displayed on TV and each pitch can be instantly measured for speed, movement and location, the notion of a human consistently misjudging balls and strikes can seem not just outdated but absurd. The outsize antics of certain umpires — presumably intoxicated by their own power — has long been a subject of fan exasperation, inspiring the derisive phrase “ump show.”

I admit I reacted to the Hernández news by watching a series of his most questionable calls, many of which made me laugh out loud. I’m not here to debate whether he was a good umpire; the data clearly indicates he was one of the least accurate.

But Major League Baseball needs fallible humans like Ángel Hernández. The ump show is as much an essential part of baseball as bone-headed errors , egregious showmanship or players angrily tossing a glove into the stands . The alternative scenario — in which baseball is adjudicated, flawlessly and bloodlessly, by machines — would make the sport less meaningful.

The Automated Balls and Strikes system, or A.B.S., is already in use in Triple-A, and the argument for embracing so-called robo-umps boils down to their accuracy. Yet the element of human judgment, as displayed by human umps, is as intrinsic to baseball as is the element of human skill, as displayed by the players. Players drop balls. They lose fly balls in the lights. They overrun bases or run through stop signals. All of this is part of the game. Blown strike calls, idiosyncratic strike zones and even flamboyantly performative umps are, and should be, a part of the game as well.

You might counter: But bad officiating adversely affects the outcome of the game. Yes, but so does bad playing. Also, it’s a game. The argument that technological proficiency should supersede human fallibility in all arenas is pernicious enough elsewhere in society, but it seems especially wrongheaded when it comes to sports, an entertaining but meaningless forum for human excellence and human foibles. There is a reason the most enduring examination of baseball’s allure ends with a mythically talented player striking out .

Infallible robot brain surgeon? Honestly, I can see the argument. Infallible robot umpires? No, thanks — I’ll take the ump show .

Paul Krugman

Inflation and the Problem of McMisinformation

The United States, like many other countries, experienced a burst of inflation in 2021-22, which has since largely subsided. But prices haven’t gone down, so almost everything costs more than it did a few years ago.

Wages, however, have risen even more, so most Americans’ purchasing power is higher than before the pandemic. But anyone who points this out gets a huge amount of pushback from people saying “Get out of your office into the real world! The price of food (it’s usually food, although it’s sometimes other stuff) has doubled!”

As it turns out, such claims about the “real world” are almost always false. A few months ago I looked at some independent estimates of grocery prices and found that they closely match the official data. No, inflation isn’t much higher than the deep state wants you to know.

Well, I now have an unexpected ally in this argument. Management at McDonald’s is apparently irked by constant claims that its prices have doubled since before the pandemic. So the company has issued a special news release about what has really happened to its prices, which are up, but not by nearly as much as the inflation truthers claim:

The price of a Big Mac, in particular, is up 21 percent since 2019, not the 100 percent some are claiming. Over the same period average wages of nonsupervisory employees — that is, most workers — rose 28 percent:

So no, McDonald’s hasn’t become unaffordable, whatever your vibes may say.

The Best Move for the Trump Jury: A Split Decision

With the jury still deliberating, it’s time for those of us who have heard every minute of this trial to place our bets. My prediction is this: Donald Trump will be convicted on nine of 34 counts of falsifying business records. He’ll go down for the nine fraudulent checks he signed in the White House in 2017 — each a piece of a broader effort to falsify business records and, ultimately, to interfere unlawfully in the 2016 election.

I could easily be wrong, of course, but here’s my reasoning. To resolve differences with a split-the-baby approach, the jury might decide that Trump’s fingerprints are literally on those checks, while the 11 false invoices, 12 false ledger entries and two false checks signed by Donald Jr. and Eric are not as closely connected to Trump, though he was the one who caused the falsification of all of them.

Jurors are showing commendable signs of diligence. It would have hurt the credibility of their verdict had they returned with one too soon on Wednesday, the day they began deliberating. On Thursday morning they reheard portions of the judge’s instructions and many pages of important trial testimony. No one has any idea if they will ask to hear more.

I think Trump deserves to be convicted on all 34 counts. But reasonable jurors could legitimately conclude that they are more comfortable with nine.

And if they reach that outcome, it could have a political effect. A conviction on fewer counts would be the best possible outcome for the country, demonstrating that the jury was unbiased and carefully considered each count, dismissing most of them.

If convicted, Trump is unlikely to win on appeal. Justice Juan Merchan has dotted his i’s and crossed his t’s, making an immediate reversal a long shot. Federal courts, including the Supreme Court, probably won’t want to get involved, and if they did, it wouldn’t be until months or years after the election.

So Trump will spend the rest of his life attacking the verdict, the judge, the prosecutors and the fair-minded residents of his hometown who determined his fate.

But at least it will be a little harder for him to spew his venom if the jury thoughtfully studies the evidence and clears him on 25 counts. His base won’t care, but I have faith that at least some swing voters who respect our jury system will conclude that this man is a criminal who should not be returned to power. Will that be enough at the margins to tip the election? No one knows for sure.

But we do know that this would probably be the only conviction of Trump before November. A careful jury verdict could help build a constituency to keep a felon out of the White House.

Time to Examine Why New York Fared Poorly Early in the Pandemic

In the coming days, House Republicans in Washington will hammer Andrew Cuomo, New York’s former governor, over his botched pandemic response, after issuing him a subpoena to appear.

That may be a political stunt, but it’s more than the Democrats who run New York State’s government have done to examine the deaths of approximately 23,000 New York City residents from Covid-19 in the first three months of the pandemic. According to an analysis by the Empire Center, a nonpartisan think tank, New York City had a higher population-adjusted Covid death rate than any state in 2020, and a rate that was 132 percent higher than the national death rate from the coronavirus.

But New York State has yet to conduct a thorough review of the actions by state, city and local officials that may have contributed to the deaths. An important bill under consideration in Albany would finally change this by creating a commission to study New York’s pandemic response. The legislation is sponsored by State Senator Julia Salazar of Brooklyn and Assemblywoman Jessica González-Rojas of Queens, whose districts were hard hit by the virus.

The commission they propose would have the authority to hold public hearings, review confidential state records and correspondence related to the pandemic, and, importantly, issue subpoenas. A review ordered by Gov. Kathy Hochul in 2022 is being conducted by a private firm, is delayed and has no such powers.

In the years since 2020, the disastrous handling of the pandemic by former President Donald Trump has been the dominant story on the issue. But New York’s early response to the virus is also worthy of scrutiny. Most widely known is a directive issued by the Cuomo administration in March 2020 ordering nursing homes to accept residents who had tested positive for the virus, leading the virus to spread even more rapidly among a vulnerable population. In 2022, a report from State Comptroller Thomas DiNapoli found that Cuomo’s administration had underreported deaths in nursing homes related to Covid-19 by more than 4,000 people. Cuomo also directed state health officials that March to give his family members special access to Covid-19 tests.

New York officials made other critical missteps. The Metropolitan Transportation Authority, which is run by the state, barred workers until early March from wearing face coverings. Former Mayor Bill de Blasio waited too long to close the city’s school system, as cities such as San Francisco had lower case counts but shuttered their schools earlier. On March 5, de Blasio discouraged the use of masks among the general public, language that was similar to guidance then from the Centers for Disease Control and Prevention and downplayed the threat posed by the virus.

New Yorkers deserve as full an accounting as possible of how and why these decisions were made.

Michelle Cottle

Opinion Writer

Team Biden’s Urgent Pitch to Black Voters in Philadelphia

At the rollout of the Black Voters for Biden-Harris effort in Philadelphia on Wednesday, Team Biden’s basic message — what it desperately needed to convey — was summed up by Vice President Kamala Harris near the end of her brief remarks: “Who sits in the White House matters. It matters!”

This may seem obvious. But as Democratic strategists and officials will tell you, a lack of urgency about, or even interest in, the outcome of this year’s presidential election — especially among younger Black voters — is one of the scariest threats the party is facing.

At this rare joint appearance, in a city where they desperately need to do well in November, both Harris and President Biden spotlighted numerous “promises made, promises kept” that they figured would be of particular interest to Black Americans.

Harris ticked through specifics, such as capping the price of insulin, forgiving billions in student loan debt, making it so medical debt doesn’t affect a person’s credit score and strengthening background checks for gun purchases.

Biden ran through even more wins — pardoning people incarcerated on charges of marijuana possession, investing in historically Black colleges, appointing the first Black woman to the Supreme Court — along with some promises about what he would do with a second term.

And both leaders brought up some of the darker moments of the Trump years, from Donald Trump’s efforts to kill Obamacare to his musings about injecting bleach as a way to cure Covid-19.

The president was not playing around. He asserted that, after Trump lost in 2020, “something snapped” in the guy, who is now “clearly unhinged.” Noting the former president’s vow to pardon the Jan. 6 rioters, Biden asked: “What do you think would have happened if Black Americans had stormed the Capitol?”

Wrapping things up, the president urged the crowd to go forth and spread the word about the urgency of this race. “Talk to your families,” he pleaded.

Biden and his team are well aware of how hard it is to break through to people who have decided to tune out an election. All of us had best hope they find a way.

Is Trump Starting to Worry About a Conviction?

Donald Trump dozed on Wednesday through a good chunk of the judge’s all-important instructions to the jury, rousing himself once to ask one of his attorneys for a bottle of Poland Spring. (His favorite drink, Diet Coke, is not allowed in the courtroom.) After Justice Juan Merchan sent the jury to deliberate, Trump chatted with Don Jr. and Alina Habba, an incompetent lawyer from an earlier trial. Then he did a quick, lip-pursed intake of breath that indicated some anxiety.

In the hallway outside, he told reporters that “Mother Teresa could not beat the charges” because of the way the judge, whom he called “corrupt,” instructed the jury. He seemed to be hinting that he believes a conviction is likely.

In fact, Merchan’s hourlong charge to the jury was standard issue in New York State and incorporated unsurprising rulings that went back to pretrial motions in March. One difference is that he read the more complicated parts twice.

The judge sided with the defense by telling the jury that if it focuses on tax offenses, it must find that Trump “willfully” intended to commit unlawful acts. But if it finds that campaign finance violations are the underlying crime, he twice mentioned that corporate contributions are banned altogether and the maximum allowable individual donation is $2,700 — a lot less than the $130,000 in hush-money that Michael Cohen paid to Stormy Daniels with Trump’s approval.

Merchan essentially instructed the jury that it can think Cohen lied about many things but find him credible on other things. And he told the jurors, “You need not be unanimous on whether the defendant committed crimes personally, by acting in concert with another, or both.” Unanimity is required only for overall conviction on each of the 34 counts.

The state law on falsifying business records requires intent to commit other crimes, so the judge spent lots of time defining that term.

In the afternoon, after nearly five hours of deliberation, the jury sent notes to the judge asking to rehear at least some of the judge’s complex instructions, which is likely to happen on Thursday. Jurors would also like another look at testimony from five weeks ago by David Pecker, the former publisher of The National Enquirer, about one particular 2016 phone call with Trump (highlighted Tuesday by a prosecution lawyer, Joshua Steinglass, in his closing argument). And jurors want to hear again why Pecker backed out of the Karen McDougal deal and how Pecker and Cohen depicted the Trump Tower meeting in August of 2015 that prosecutors argue was the birth of the conspiracy.

Trump’s defense team also focused on that meeting, insisting that it was commonplace for candidates to “work with the media” to squelch sex stories, as Pecker said he did with Arnold Schwarzenegger and Rahm Emanuel. (It is not commonplace.)

Because the jurors are now practiced at poker faces, we aren’t learning which way they are moving, only that they are diligently examining the evidence.

Nikki Haley’s Valentine to Civilian Death

It was a sweet little heart, the kind you might draw on a Valentine’s Day card. “America 💜 Israel Always,” the author wrote, above her handwritten signature: “Nikki Haley.” How lovely.

Except it wasn’t written on a greeting card. Haley drew the heart in purple ink on a 155-millimeter artillery shell, the kind that the Israeli Army has routinely loaded into howitzers and fired on Gaza in the hopes of eradicating Hamas but resulting in the mass deaths of civilians. Tens of thousands of these shells have rained down on Gaza since the Oct. 7 massacre, and when they explode they send countless metal fragments in every direction, with a casualty range of between 100 and 300 meters . A coalition of human rights groups say that this particular artillery weapon is so indiscriminate that its use in heavily populated areas like Gaza violates international humanitarian law.

But that wasn’t all that Haley wrote. Above the little heart was a message of savage revenge: “Finish them!”

“Finish Them, America ♥️ Israel Always!” Message from @NikkiHaley , written on an Israeli missile intended for Hamas. pic.twitter.com/DgPQYNvkWM — Team Nikki Haley (@NikkiHaleyHQ) May 28, 2024

Haley, the former governor of South Carolina, made it clear on social media that both the inscription and the shells were intended for Hamas. But her scrawled fondness for bloodshed — with little apparent concern for whose blood will actually be shed — sends a more important message to American voters.

A huge number of progressive voters are furious at President Biden for not doing more to stop Israel’s assault on Gaza. And it’s true that many of those artillery shells were supplied by the United States. But if those voters think that the situation in Gaza will change if they sit out the election and allow Donald Trump and other Republicans to be elected, they don’t really understand what’s coming. Because it would be a lot worse.

Haley lost her bid to become the Republican nominee for president because she was seen as too moderate for a party that still prefers Trump’s recklessness. When it comes to issues like Israel, most of the party is further to the right than the author of “Finish them!”

Biden should have done much more to use American leverage on Israel to reduce the civilian toll in Gaza. But Republicans pound him every day for withholding an arms shipment to Israel to prevent it from being used to attack Rafah, in the Gaza Strip. He has never signed his name on a lethal explosive device and expressed a hope that it would kill. There’s a big difference.

Neel V. Patel

Opinion Staff Editor

The Stalled Pandemic Accords Offer an Opportunity for Vaccine Equity

For more than two years, the member states of the World Health Organization have been meeting to iron out an agreement on how to prevent and respond to future pandemics. The text of the accord was supposed to be finalized last Friday, for nations to formally approve it this week during the World Health Assembly in Geneva.

That deadline came and went, and negotiations on the accord have stalled because of disagreements about global vaccine availability. Countries cannot agree on whether to prioritize making new treatments more available to poor countries or certain intellectual property rights of vaccine manufacturers in wealthy countries instead. There’s a stark division between the haves and the have-nots of the global stage.

On the surface, the breakdown in talks is a familiar story of international diplomacy. But it also presents an opportunity. Wealthier nations could use this moment to reverse course on the agreement’s more rushed, toothless measures and turn it into something consequential and lifesaving.

Not even three years ago, richer countries like the United States bought enough Covid-19 vaccine stock for twice its population ; Canada, for five times its population. Poorer countries came last , relying on donated vaccines and Covax, the global vaccine-sharing scheme. Vaccine hoarding among wealthy nations probably led to more than a million deaths in 2021 alone . Many countries on the African continent suffered an especially slow rollout, causing their economic recoveries to lag those of the rest of the world .

Besides the moral argument that developed countries should do more to help developing ones, there’s a practical argument to make: Pandemics don’t care about national borders. If an infectious disease is allowed to thrive in one region, travel and migration ensure that it will inevitably threaten surrounding regions as well, putting the globe at further risk.

If our leaders want to avoid a fate similar to 2020, they need to guarantee that essential vaccines and treatments are available wherever they are needed.

Prosecutors Leave the Jury With a Mountain of Evidence Against Trump

Humor helps, especially if you are delivering a five-hour speech.

Joshua Steinglass of the prosecution team knew he was taking a risk by “trading brevity for thoroughness” in his closing argument in the Donald Trump felony trial in Manhattan; besides being exhausted after an 11-hour day, jurors might conclude “the people” (the formal name for the prosecution) were not sure enough about their case to avoid piling on.

So Steinglass copped to “beating a dead horse” and helped neutralize the defense’s best point with a little playacting.

In the morning Trump’s lead attorney, Todd Blanche, again called Michael Cohen a liar for claiming he phoned Trump on Oct. 24, 2016, to talk to him about hush money for Stormy Daniels when text chains showed he wanted to ask Keith Schiller, Trump’s bodyguard, about a 14-year-old prank caller who was harassing him.

To explain that Cohen could have talked about both , Steinglass assumed Cohen’s voice and cradled an imaginary phone:

“Hey, Keith, how’s it going?” he asked, imitating Cohen. “Hey, is the boss near you? Can you pass him the phone for a minute?”

Then Steinglass turned self-effacing — “Sorry if I didn’t do a good job” — before proving that was only one of about 20 times in October alone that Cohen updated Trump about his progress in hushing Daniels, thereby helping to save Trump’s sagging campaign.

Steinglass went to great lengths to show that his case did not rely entirely on Cohen. Steinglass returned again and again to the first-week testimony of David Pecker, a former publisher of The National Enquirer, who implicated Trump directly in a conspiracy to interfere in the 2016 election. And Steinglass assembled, disassembled and all but cleaned what he called “the smoking gun” — the handwritten notes detailing Trump’s scheme to disguise his reimbursement of Cohen as legal expenses.

The long faces in the Trump guest section reflected the sense in the courtroom that Trump’s story that the $420,000 he paid to Cohen was really a legal retainer will not fly. Steinglass showed that Trump himself admitted in court documents and other records that it was a reimbursement.

Steinglass also proved that “Michael Cohen is no rogue actor” and that in 2018 Trump, Rudy Giuliani and the lawyer Robert Costello treated Cohen like a mob rat as part of the cover-up. This was La Casa Blanca meets La Cosa Nostra.

The defense has a better shot at creating doubt that Trump intended to commit a crime, but even here, Steinglass had a heap of evidence to shovel in the jury’s direction.

The judge allowed most of it until the prosecutor overreached by urging jurors not to let Trump get away with shooting someone on Fifth Avenue, evoking his famous line about what he could get away with.

Just after the objection to that was sustained by the judge, Steinglass finally stood down, and we all dragged off to bed. The case finally goes to the jury on Wednesday.

Farah Stockman

Netanyahu Is Sorry/Not Sorry for the Killings in Rafah

I often tell my 8-year-old daughter that saying “sorry” doesn’t cut it if she continues the behavior that she’s apologizing for. It’s a basic lesson that kids learn. World leaders need to learn it, too, apparently.

After facing international blowback for the Israeli military strike that burned dozens of people alive in their tents in a refugee camp in Rafah on Sunday, the Israeli prime minister, Benjamin Netanyahu, called the civilian deaths a “tragic mishap.” He also said that his government was making “utmost efforts not to harm innocent civilians” and that mistakes would be investigated.

It reminded me of the awfully similar statement he gave in April, after the Israeli military attacked a convoy of World Central Kitchen staff members who had just unloaded food aid at a warehouse in Gaza. Those deadly airstrikes took place even though the World Central Kitchen workers drove in a clearly marked convoy and had meticulously coordinated their movements with the Israeli military. After an international outcry, Netanyahu issued a statement calling the deaths “a tragic accident” that “happens in war.”

“We are conducting a thorough inquiry and are in contact with the governments,” the statement read. “We will do everything to prevent a recurrence.”

But by that time, the sheer number of attacks on aid workers and on Gaza civilians seeking aid raised real questions about whether we have been witnessing intentional killings or “reckless incompetence,” as Christopher Lockyear, an official with Doctors Without Borders, noted .

On the side of reckless incompetence, there was that time in December when Israeli soldiers fired on three unarmed men waving white flags — only to discover that they were Israeli hostages who had managed to break free of their captors. At that time, Netanyahu’s office released a statement that called the killings “an unbearable tragedy.” The statement pledged to “learn the lessons” to ensure that it wouldn’t happen again.

How many apologies will be issued and investigations pledged before this God-forsaken war ends? Netanyahu’s list of international apologies keeps growing. But the attacks on Rafah — and the unspeakable suffering of Palestinian civilians — continue .

Frank Bruni

Pope Francis’ Remarkable Act of Contrition

I’m not accustomed to apologies from popes. Aren’t they infallible?

Yes, I know, that term doesn’t have practical, colloquial application — it doesn’t mean that they never bungle math problems or lose track of where they hung their robes. But the general notion or mythology of infallibility reflects a kind of papal authority and aloofness that discourages any real-time revisiting of false steps, any open regret for errant syllables.

“I’m sorry” belongs to the political realm (or at least did until Donald Trump came along). Popes inhabit a higher plane.

So a Vatican statement on Tuesday that Pope Francis “extends his apologies” to anyone offended by something he recently said is a big and surprising deal. It’s all the bigger and more surprising because Francis was apologizing for insulting gay people, and for most of my 59 years, Roman Catholic leaders were more concerned with condemning or converting or chiding or hiding us than with making sure our feelings weren’t hurt.

In a closed-door meeting with Italian bishops last week, Francis reportedly responded to a question about whether openly gay men should be admitted to seminaries by saying that those training grounds for future priests were already too crowded with “frociaggine,” a crude Italian slur.

I’m disappointed that he used it, contradicting past statements of his that urged respect for gay people and his decision last year to allow priests to bless same-sex couples . I don’t know whether he was disclosing his own lingering bigotry or trying to curry favor with the conservatives around him.

But I know this: Another pope in a prior era wouldn’t have been so quick to do damage control. Another pope in a prior era mightn’t have felt that any damage was done.

And even Francis could have decided simply to ignore the media attention to his offensive language until it died down. Popes are expected to worry not about the news cycle but about eternity. What’s more, he would have pleased some of his sternest critics by moving on. They complain that he has done too much outreach to L.G.B.T.Q. people and been too indulgent of them.

His apology speaks to the kind of pope that he, at his best, has been: one who means to heal wounds. But it says even more about an altered church in a changed world, where gay people still endure taunts aplenty but also encounter unexpected moments of grace.

The Trump Team’s Inept Closing Argument Blew Up

If Donald Trump becomes a felon in the coming days, he and his defense team can partly blame themselves. Throughout the trial they offered implausible arguments against the prosecution’s case, and on Tuesday Trump’s lead attorney, Todd Blanche, slipped an I.E.D. into the end of his closing argument that blew up in his face.

“You cannot send someone to prison based on the words of Michael Cohen,” Blanche said, in a bid to make jurors think it was their role to decide if a president should be incarcerated.

“Saying that was outrageous,” Justice Juan Merchan told Blanche after the jury left for lunch. Mentioning sentencing to gain sympathy with jurors who have no say in punishment “is simply not allowed,” he said, and that it was “hard for me to imagine how that was not intentional.”

The defense got more than a tongue-lashing. After lunch, Merchan turned to the jurors and told them why they had to ignore this sneaky move — not a good final look for the defense.

In his three-hour closing argument, Blanche gave jurors a few places to explore reasonable doubt but mostly swung wildly and set up the prosecution for better arguments in the afternoon.

My favorite dumb moment: “Guess who else you did not hear from in this trial?” Blanche asked. “Don and Eric. Is there some allegation that they are part of a conspiracy?” No, counselor, but the jury will likely wonder why the defense called Robert Costello, who was destroyed on cross-examination, instead of Trump’s own sons.

Blanche huffed and puffed to discredit the two possible “smoking guns” offered by the prosecution. The first consists of the scrawled notes of Allen Weisselberg, former financial head of the Trump Organization, breaking down the $420,000 that Trump paid Cohen in 2017. Weisselberg wrote “gross it up” in reference to doubling the $130,000 in hush money for tax purposes. That “is a lie,” Blanche said, using a word he would employ more than 30 times in his closing argument, to diminishing effect.

But it wasn’t a lie. The former controller of the Trump Organization had confirmed on the stand that the numbers and “gross it up” were in Weisselberg’s own hand.

The other smoking gun involves a call Cohen taped, during which Trump said “150” in reference to the hush money for Karen McDougal. While trying and — to my mind — failing to establish that Cohen’s phone was tampered with, Blanche played the tape and challenged the idea that Trump even said “150” and that Trump saying “cash” on the tape had anything to do with hush money. Jurors will presumably listen to the tape and decide for themselves. Believe me, you can hear “150.”

Blanche ended his closing argument by telling jurors that if they focus on the evidence, “this is a very easy and quick not-guilty.” Insulting the jury’s intelligence? Not smart.

Michelle Goldberg

The Trump Team’s Big Lie About the ‘Access Hollywood’ Tape

In his closing argument on Tuesday, Donald Trump’s lead defense attorney, Todd Blanche, repeatedly tried to sell a revisionist history of the infamous “Access Hollywood” tape, in which Trump was recorded boasting of his penchant for sexual assault. In the felony case against Trump, the “Access Hollywood” tape is important because, in the story the prosecution is telling, it’s the reason Trump was desperate to quash Stormy Daniels’s story.

“The government wants you to believe that the release of that tape from 2005 was so catastrophic to that campaign that it provided a motive for President Trump to do something criminal,” he said.

Attempting to undercut that narrative, Blanche insisted that it really wasn’t that big of a deal. It caused, he said, a “couple days of frustration and consternation, but that happens all the time during campaigns.” He added: “The ‘Access Hollywood’ tape is being set up in this trial to be something that it is not.”

This is insultingly and obviously untrue. As the longtime Trump aide Hope Hicks testified about that moment in the 2016 campaign, “I think there was consensus among us all that the tape was damaging, and this was a crisis.”

We now know that a critical mass of voters doesn’t care about Trump’s misogyny and predation, but we didn’t know that then. One job of the prosecution, which begins closing arguments Tuesday afternoon, will be to take the jury back to a more innocent time before Trump’s election, when people still imagined there were Republicans with a capacity for shame.

There’s Nothing Simple or Obvious About Trump’s Trial Defense

During closing arguments in Donald Trump’s felony trial on Tuesday morning, his lawyer Todd Blanche said, “There’s a reason why, in life, usually the simplest answer is the right one.”

I found this an odd approach, because to believe his theory of the case requires accepting several improbable things. First, although it’s not legally germane, Blanche reiterated, perhaps at the insistence of his client, that Trump “has unequivocally and repeatedly denied” any encounter with Stormy Daniels. And rather than simply arguing that Trump didn’t know about the scheme to reimburse Michael Cohen for the payoff to Daniels, he appears to be arguing that no such scheme existed.

Cohen, said Blanche, had a verbal retainer agreement in 2017 to serve as Trump’s personal attorney, and that’s why he was paid $420,000. If that’s the case, it’s hard to imagine why Cohen pleaded guilty and served prison time in connection with the hush-money payment.

Blanche’s argument has been internally inconsistent. First, he insisted that Trump, being busy as president, didn’t always look at the checks he signed. Then, trying to discredit the idea that Trump would reimburse Cohen $420,000 for a $130,000 payment — which Cohen has claimed was grossed up to include taxes and a bonus — Blanche pointed to “all the evidence you heard about how closely President Trump watches his finances.”

During a long digression about the National Enquirer’s practice of “catching and killing” stories, he insisted that there had never been a “catch and kill” plot involving the Playboy model Karen McDougal, implying, I think, that her deal with the publication was on the level. “She wanted to be on the cover of magazines, she wanted to write articles,” Blanche, said and that’s what she did.

Obviously, I have no idea what the jury is thinking. But given the implausibility of the narrative that Trump’s defense is spinning, it just seems weird that Blanche is invoking Occam’s razor.

Patrick Healy

Deputy Opinion Editor

How Quickly Would a Trump Verdict Sink In for Voters?

Each week on The Point, we kick things off with a tipsheet on the latest in the presidential campaign. Here’s what we’re looking at this week:

The most consequential week of Donald Trump’s criminal trial in Manhattan has arrived: The jury could begin deliberating in the next two days. We’ll also get insight shortly about Justice Juan Merchan’s instructions to jurors — basically, a clearer picture of what options they have for a verdict. As for the political impact of any decision by the jury, I think that will take weeks to become clear as Americans learn and absorb the news — as suburban women outside Philadelphia, for instance, weigh the verdict and their feelings about Trump against their views on the economy or abortion rights.

It takes time for voters to process big news, and opinions can shift with time. Part of why James Comey’s Oct. 28, 2016, letter about Hillary Clinton’s classified email was so politically damaging to her was that it came as many people were casting early votes and others were making up their minds ahead of the Nov. 8 election. The Trump verdict will be historic, but the election is five months away. How voters feel about the verdict could surely change in that time.

We’ll also start getting a clearer picture this week about whether Robert F. Kennedy Jr. will qualify to join the June 27 debate between President Biden and Trump. There’s a good explainer here boiling down how Kennedy needs to make the November ballot in a bunch more states first to make the cut for the debate. Given the various rules, I don’t think there’s much time for him to make the June debate; he may have a better shot at the September debate. Either way, I can’t see the Biden and Trump campaigns eager to have him onstage — they don’t want anything distracting voters from seeing the flaws and fumbles in the other guy, and R.F.K. Jr. will be one big distraction.

I’m preoccupied with the Biden-Trump fight for Pennsylvania and whether Biden can borrow from the winning political playbook of Gov. Josh Shapiro, who won a 15-point landslide in 2022. Biden is trailing Trump by a couple of points in the state polling average. As in other swing states, Biden needs to do far better than he’s currently polling with young voters and nonwhite voters, and with voters in Philadelphia and its suburbs. So keep an eye on Biden’s campaign trip to Philadelphia on Wednesday and his pitch for why Americans should want another four years of his presidency.

Trips like Biden’s Philadelphia event are planned weeks in advance, but as it happens, this one will probably happen just as the Trump jury is deliberating on Trump’s fate (or returning with a verdict). The split screen of Biden heralding Ben Franklin and Trump attacking jurors is a news cycle the Biden campaign badly wants.

Bret Stephens

What’s Spanish for ‘Chutzpah’?

This week’s announcements by the governments of Ireland, Norway and Spain that they will recognize a Palestinian state are drawing predictable reactions from predictable quarters. Some see them as useful rebukes to Prime Minister Benjamin Netanyahu’s war strategy in Gaza that will further isolate Israel. Others, including me, view them as feckless gestures that reward Hamas’s terrorism.

That’s a column for another day. For now, it’s enough to note the Spanish government’s sheer nerve.

Though Spanish public opinion overwhelmingly supports swift recognition of Palestinian statehood, it’s another story when it comes to Spain’s own independence movements. In 2017 the regional government of Catalonia held a referendum, declared illegal by Spain’s Constitutional Court , on the question of Catalan independence. Though turnout was low — in part because Spanish police forcibly blocked voting — the Catalan government said nearly 90 percent of voters favored independence.

The central government in Madrid responded by dismissing the Catalan government, imposing direct rule. Two years later, under the current left-wing government of Pedro Sánchez, Spain sentenced nine Catalan independence leaders to prison on charges of sedition, though they were later pardoned. This year the lower house of the Spanish Parliament voted to grant amnesty to those involved in the 2017 campaign as part of a deal to prop up Sánchez’s government, despite a Senate veto. Seventy percent of the Spanish public opposes the amnesty .

Catalans aren’t the only ethnic minority in Spain that has sought independence, only to encounter violent suppression. In the 1980s the Spanish Interior Ministry under a socialist government responded to the long-running Basque separatist movement with state-sponsored death squads, notoriously responsible for a string of kidnappings, tortures and assassinations. The Spanish government called the separatists terrorists — as indeed some were — though their tactics look tame compared with Hamas’s. By the time the conflict ended in 2011, it had claimed more than 1,000 lives.

Spain possesses two cities on the African continent, Ceuta and Melilla, both of which are claimed by Morocco and have been stormed by African migrants seeking entry into the European Union. They are protected by extensive border fences and fortifications strikingly reminiscent of Israel’s breached border fence with Gaza.

There are many other independence movements throughout Europe, from Scotland to Flanders to Corsica and the Balkans. Many of these movements tend to have affinities with Palestinians, for reasons that are obvious. More difficult to explain are governments that suppress independence seekers at home while applauding those abroad. Some might call it deflection. To others, it looks like hypocrisy.