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A Qualitative Study of Smoking Behaviors among Newly Released Justice-Involved Men and Women in New York City
Long-term effects of cigarette smoking result in an estimated 443,000 deaths each year, including approximately 49,400 deaths due to exposure to secondhand smoke. Tobacco is a major risk factor for a variety of chronic health problems, including certain cancers and heart disease. In this article, authors present qualitative findings derived from individual interviews with men and women who were incarcerated in New York state and New York City. Participants were 60 racially and ethnically diverse men and women ages 21 through 60 ( M = 46.42, SD = 6.88). Of the participants interviewed, 91.7 percent released from a smoke-free correctional facility resumed cigarette smoking and 8.3 percent remained abstinent. Daily consumption ranged from smoking four cigarettes to 60 cigarettes. The four themes that emerged from the study were (1) lifetime exposure to cigarette smoking influences smoking behavior; (2) cigarettes help relieve stress and are pleasurable; (3) there is a relationship between access, availability, and relapse; and (4) smoking cessation strategies are available. Negative influences from participants’ families and peers, stressful housing situations, and mandated programs emerged from this study as key challenges to abstaining from smoking cigarettes. Involving family members and partners in smoking cessation interventions could influence newly released justice-involved men and women not to resume cigarette smoking and possibly maintain long-term abstinence.
Cigarette smoke results in an estimated 443,000 deaths each year, including approximately 49,400 deaths from exposure to secondhand smoke ( U.S. Department of Health and Human Services [HHS], n.d. ). Tobacco use is a major risk factor for a variety of chronic health problems, including certain cancers and heart disease ( HHS, n.d. ). An estimated 19.3 percent of the U.S. adult population (45.3 million individuals) were smokers in 2011; however, the smoking prevalence in previously incarcerated populations has consistently been shown to be substantially greater than in the general population, with around 60 percent to 80 percent opting to smoke on release to the community ( Cropsey, Jones-Waley, Jackson, & Hale, 2010 ; King, Dube, Kauffman, Shaw, & Pechacek, 2011 ).
The Federal Bureau of Prisons made all facilities under its control 100 percent smoke free in July 2004. In state prisons and jails, correctional officials have adopted varying degrees of smoke-free resolutions that promote either smoke-free policies or prohibit smoking in their facilities altogether ( American Nonsmokers’ Rights Foundation, 2015 ). Despite the pervasiveness of U.S. correctional smoke-free policies, there are still many questions regarding their effectiveness. For one, enforcement of and compliance with these policies are highly variable and, as a consequence, banning cigarettes does not ensure abstinence from tobacco use on release from a correctional facility ( Foley, Proescholdbell, Herndon Malek, & Johnson, 2010 ). For instance, Cropsey and Kristeller (2005) found that 76 percent of the prisoners who smoked prior to a prison smoking ban still reported some level of smoking one month after being released from the facility. In addition, even if smokers are able to quit while incarcerated, mounting evidence suggests that forced abstinence in prison does not result in sustained nonsmoking behavior on release ( Cropsey & Kristeller, 2005 ; T. Lincoln et al., 2009 ). T. Lincoln et al. (2009) found that 97 percent of prisoners relapsed within six months of release back to their communities.
SMOKING BEHAVIORS AND INTENTIONS TO QUIT IN JUSTICE-INVOLVED POPULATIONS
A better understanding of the smoking behaviors and intentions to quit among individuals involved in the criminal justice system may be critical to reduce tobacco use in this population. Cropsey and Kristeller (2005) found that individuals who continued to smoke after release from a tobacco-free facility were more nicotine-dependent and reported more withdrawal symptoms, even after accounting for baseline nicotine-dependence and baseline withdrawal scores ( Cropsey & Kristeller, 2005 ). In another study, Voglewede and Noel (2004) found that smokers with a strong desire (craving) for tobacco were more likely to intend to smoke on release from jail. Interestingly, they found no relationship between intent to smoke and length of incarceration or level of nicotine dependence ( Voglewede & Noel, 2004 ).
We know very little about how individuals who are supervised in the community (parole or probation) rebuild their lives with respect to their tobacco habits after being released from smoke-free correctional facilities. To learn more about this issue, we asked the participants (a racially and ethnically diverse group of men and women released from New York correctional facilities) to describe their smoking behaviors before, during, and after incarceration and their intentions to quit tobacco use during these same time periods.
In this qualitative study, we explored the following three research questions: (1) What are the smoking behaviors and intentions to quit tobacco in justice-involved populations? (2) What are the social characteristics that support or encourage tobacco resumption? (3) What smoking cessation programs, if any, are provided to, or sought out by, the study participants upon their release from tobacco-free correctional facilities?
Qualitative data presented here were gathered from men and women who were formerly incarcerated in a New York state prison or the Rikers Island jail in New York City (NYC), and who returned to reside in the Bronx, New York. Among the 60 participants were 13 black men and 19 black women, 17 Latino men and eight Latina women, and three white women (but no white men), and they ranged in age from 21 through 60 years ( M = 46.42, SD = 6.88). Of the participants interviewed, 58 percent were released within one year; 66 percent served in both prison and jail; 23 percent served jail time only; 10 percent served in prison only; and one participant was involved in an alternative-to-incarceration program. The age of first incarceration ranged from 18 to 52 years ( M = 29.87, SD = 14.75).
In terms of the participants’ levels of education, 45 percent of participants (11 women and 16 men) did not complete high school or obtain a GED diploma; 35 percent of participants (11 women and 10 men) graduated from high school or obtained a GED; and the remaining 20 percent (eight women and four men) attained some college education in the form of some college credits, a bachelor’s degree, or a master’s degree. The age when the participants left school ranged from four years old to 24 years old ( M = 15.95, SD = 3.34). The participant who indicated that he or she left school at age four did not have any formal schooling. In terms of participants’ marital status, nearly half (48.3 percent; 15 women and 14 men) were single or never married; 25 percent (four women and 11 men) were married; and the remaining were divorced (five women and three men), separated (five women), or widowed (one woman and two men). The number of children among the participants ranged from zero to 10 children ( M = 2.40, SD = 1.80). The institutional review boards at Albert Einstein College of Medicine and Columbia University approved the research procedures, and the first author obtained a National Institutes of Health Certificate of Confidentiality.
In New York, approximately 74 percent of individuals in the custody of the Department of Corrections and Community Supervision are black or Latino ( State of New York Department of Corrections and Community Supervision, 2012 ). Of the 13 NYC community districts that have the highest rates of incarceration (greater than 10 inmates per 1,000 residents), six are located in the Bronx. In the district with the highest incarceration rate of 12 per 1,000 residents (containing Morris Heights, University Heights, Fordham, and Mt. Hope), 41 percent of the residents of these district are impoverished, and 58 percent receive public assistance ( Mellow et al., 2008 ). Overall, 31 percent of Bronx residents live below the federal poverty level.
The Bronx has one of the highest rates of current smokers in NYC at 18.0 percent, compared with 15.2 percent in Manhattan ( New York City Department of Health and Mental Hygiene, n.d. ). Because of the borough’s higher-than-average smoking rate and its significant population of formerly incarcerated individuals living in the county, the research team chose the Bronx as a model setting for this study. The borough offers a large population of study participants to draw from; lessons obtained from this study’s focus areas can thus be more broadly applied to other communities of color.
The semistructured interview format was flexible to allow participants to respond to questions naturally, but structured enough to keep the discussion on relevant topics. The interview questions were developed by the first author. The following are examples of the interview questions:
- How many packs of cigarettes do you smoke daily?
- What type of cigarettes do you typically smoke?
- Could you please describe a situation when you need a cigarette?
- How long were you smoking before you were incarcerated?
- How does smoking cigarettes make you feel?
- Why do you smoke now?
- How do you obtain money to buy cigarettes?
- Have you tried to quit? (Probe question: If yes, what have you tried—nicotine patch, therapy, self-help, and medication?)
- When was the last time you tried to quit?
- How has your smoking behavior changed since coming home from prison or jail?
- How much do you spend per week on cigarettes?
- Do you have family members who smoke cigarettes? (Probe for any members dealing with cancer-related to cigarette smoking or tobacco use.)
Participants were recruited through flyers advertising the study placed in criminal court buildings, drug and rehabilitation centers, and social services agencies working with individuals involved in the criminal justice system. Potential participants were asked to contact the research office to determine eligibility. The first author and her research team (master’s-level public health and social work students who were trained in qualitative research methods) recruited study participants and conducted the individual interviews during a six-month period in 2011.
To participate in this study, individuals had to meet the following eligibility criteria: (a) self-identify as a male or female; (b) age 18 years or older; (c) reside in Bronx, New York; (d) under community supervision (parole or probation); (e) report no previous diagnosis of cancer; (f) report substance use history; (g) provide informed consent; and (h) agree to the interview being digitally recorded. We also invited participants to refer friends and peers who met eligibility criteria. The interviews ranged in length from 90 to 120 minutes. All interviews were conducted in a private meeting space, and all participants were compensated in cash for their participation.
Data Analysis
The digital recordings of the interview data were transcribed verbatim by a professional transcriptionist; NVivo 10, a qualitative software package, was used to manage and code the data. The first and second authors analyzed the data using content analysis to develop the smoking behavior codebook. Categories were developed and refined using passages retrieved from the transcripts; the data that were in close associations were grouped together and assigned a tentative code ( Hsieh & Shannon, 2005 ).
We created a table in Microsoft Excel that listed the first-level codes, second-level categories, and potential subheadings as an initial template of the codebook. This stage involved identifying relationships among the codes and developing connections or relationships within the codes that we previously identified. We also created definitions and used the passages to illustrate the inclusion and exclusion criteria for each code. We reread the transcripts related to smoking and the codes selected, followed by the construction of the codebook; we used statements from the participants to support and define the code ( Hsieh & Shannon, 2005 ). The codebook allowed the authors to code passages in which participants described their smoking behaviors and intentions to quit. When discrepancies occurred during coding, we met to discuss the differences until consensus was reached.
We assessed the credibility of our analyses in several ways. First, we reviewed the findings with several participants to ensure that the analyses and interpretations of the data reflected the interviewees’ own experiences and perceptions, thus minimizing researchers’ biases ( Kirk & Miller, 1986 ; Y. S. Lincoln & Guba, 1985 ). Second, we were intensely engaged in the research, conducting multiple reads of the transcripts, met for several months to address discrepancies in the coding process, and ensured that our analyses and interpretations were rooted in the data. Third, we used bracketing to ensure that our assumptions and beliefs did not influence our analysis ( Creswell & Miller, 2000 ). This included writing memos throughout data analyses and reflecting on how we engaged the data.
Of the participants released from a tobacco-free correctional facility, 91.7 percent ( n = 55) resumed smoking cigarettes after release; only 8.3 percent ( n = 5) remained abstinent.
Cigarette Smoking Habits
There were many commonalities in the smoking behaviors of the participants. The majority of the interviewees smoked Newport menthol cigarettes (including “loosies” or single Newport Menthol cigarettes and bootleg Newports), followed by rollies and natural cigarettes. The daily smoking behaviors ranged from four cigarettes (light smoker) to 60 cigarettes (heavy smoker), the equivalent of three packs per day ( M = 17.2, SD = 12.81).
The majority of the participants spent $11.50 to $16.00 per day on a pack of cigarettes; others spent $0.50 or $0.75 for a single cigarette, or $7.00 on bootleg cigarettes. Bootleg cigarettes are cheaper because they are sold with a counterfeit tax stamp or with no tax stamps at all. On average, participants spent $40 to $50 per week on cigarettes. Some participants borrowed cigarettes from friends.
A little over half (51.6 percent) of the participants claimed they had no knowledge about the health effects of smoking. Despite this, 12 participants reported tobacco-related illnesses, including heart disease, asthma, and advanced emphysema. In addition, six participants reported that lung and bone cancer ( n = 5) and myocardial infarction ( n = 1) were the causes of death of a family member.
The four most salient themes that emerged from the data were (1) lifetime exposure to cigarette smoking influences smoking behavior; (2) cigarettes help relieve stress and are pleasurable; (3) there is a relationship between access, availability, and relapse; and (4) smoking cessation strategies are available.
Theme 1: Lifetime Exposure to Cigarette Smoking Influences Smoking Behavior
Participants were exposed to secondhand smoke primarily through caregivers. Many of the participants began smoking cigarettes at a very early age (from five through 15 years old, M = 12.5). When asked how he acquired his first cigarettes, a Latino male participant, age 43, incarcerated for over 15 years, and released less than six months prior to the interview, said, “I stole my mother’s cigarette. And I just went and I smoked it, and from then on after I puffed a couple of times, there it goes.” A Latina female participant, age 43 years, incarcerated for less than six months in a jail facility, and released less than six months prior to the interview, said, “My mother used to stay with us ... she used to put cigarettes in our mouths because we did not know how to smoke. And I learned how to steal cigarette[s] from my mother.” Unfortunately, the participants did not have family members teaching them about the importance of not smoking; instead of being talked to about the dangers of smoking, it was more common for participants to have family members who smoked. The vast majority of the study participants (70 percent) stated that having smokers as family members influenced their smoking behaviors during the reentry process. Several participants discussed having family members who are currently smokers. For example, “My mother, she smokes a lot. And, I hear her coughing at night. I say, ‘Mommy, you smoke a lot. We gotta stop smoking.’ She gets angry. I say we need to,” noted a black male participant, age 48 years and involved in an alternative-to-incarceration program.
Some participants expressed concern that many people in their family smoked. For example, a black female participant, age 45 years, who experienced both jail and prison time and was released less than three years from the time of the interview, said, “My uncle smokes. My sister smokes, my nephew smokes, and my son smokes. But they don’t smoke like chain smokers. I don’t know how they smoke. I know that they smoke; they indulge in cigarettes every now and then.” Another participant said:
My brother smokes. Well, all my brothers smoke. ... And, one of them has a pacemaker right now. Well, he got to really stop smoking. I think if I was at that level, I’d really stop smoking ... but you know, I been smoking since I was like 14 . (black male participant, age 46, experienced jail and prison, and was released less than six months prior to the interview)
Family members play a very important role in justice-involved men and women’s ability to remain smoke-free after release from a correctional facility.
Theme 2: Cigarettes Help Relieve Stress and Are Pleasurable
Men and women who are newly released from a correctional facility face multiple challenges related to reintegration to society. Some face legal barriers to receiving public benefits; others struggle with mental illness, physical health conditions, substance use problems, or disability; and many are unemployed and often become homeless if there is limited transitional housing or family supports ( La Vigne & Kachnowski, 2003 ). Community reentry is a stressful time for many men and women involved in the criminal justice system. Because of the extreme challenges to meet basic needs (such as stable housing, employment, and food), participants may engage in risky behaviors or old vices to cope with the stressful circumstances ( Luther, Reichert, Holloway, Roth, & Aalsma, 2011 ). Although the participants did not engage in substance use, primarily because the majority of them were under community supervision, most ( n = 55) reengaged in cigarette smoking on release and while in reentry.
In this study, a little over half of the sample ( n = 35) denoted psychological pleasure in smoking cigarettes. The most common explanations for smoking cigarettes were “brings pleasure,” “relaxing,” “calms me down,” “reduces anxiety,” “puts me at ease,” and “makes me feel good.”
Study participants discussed the benefits of smoking cigarettes as they navigated the community reentry process. For over half of the participants, cigarettes helped to regulate and ease stressful experiences. A black female participant, age 47 years, who spent 16 months in jail and was released less than six months prior to the interview, was asked by the interviewer what she did when she “started feeling extra stress,” and rather than talk to a counselor, friend, family member, or probation officer about what she was feeling, she smoked more. Another participant, Latino, male, age 50, incarcerated for less than 30 days in jail and released less than six months prior to the interview, reported, “I feel more relaxed. You know, cigarettes relax me. I feel well ... I feel better.” Chain smoking was mentioned by the participants as a method that they used to destress.
Theme 3: Relationship between Access, Availability, and Relapse
Inmates released from prison and under community supervision who need a place to live may be sent to a structured transitional housing facility or a recovery housing facility, and parolees residing in these facilities have access to supportive services. However, some of these housing facilities are not smoke-free properties. In our study, a number of participants said that they reengaged in cigarette smoking immediately because cigarettes were readily available when they were released to transitional housing.
Interviewer: When did you pick up the first cigarette when you got home?
Interviewee: When I got to Facility A. I just went, got a loosie, and smoked it. I was dizzy as hell. I don’t know, I guess it was something to do.
Interviewer: Many people around you?
Interviewee: Yeah, everybody, almost all the girls there smoke. (Latina, female, 39 years old, eight months incarcerated in jail, and released less than six months prior to interview)
Living with other parolees in transitional housing facilities that are not smoke-free properties may be associated with reengaging in cigarette smoking. For instance, a black male participant, age 50 years, who spent less than six months in prison and was released less than six months prior to interview, stated, “Well, right now there are a lot of guys in the house that, you know, a lot of ’em are working, so it’s easy to get a cigarette sometimes. It’s not hard to get a cigarette.” Overall, men and women who are returning from correctional facilities are faced with overwhelming challenges to maintain a smoke-free lifestyle. They are paroled to programs and housing facilities where smoking is hard to resist.
Theme 4: Smoking Cessation Strategies Are Available
The final theme that emerged in the study was a sense of not resigning to a feeling of hopelessness because of one’s circumstances. Despite being exposed to a lifetime of cigarettes, returning to family members who are current smokers, and being mandated to programs and housing facilities that are not smoke-free, some participants felt that they had a choice whether to feel trapped or find approaches to remain smoke free. The last successful attempt for participants was when they were incarcerated and forced to quit. At least half of participants were working toward reducing the number of cigarettes smoked per day, mainly because of cost. Because cigarette smoking is an expensive habit, some participants described using the following strategies to save money and reduce the frequency of smoking cigarettes: “smoking less,” “using the nicotine patch,” “asking a physician for [smoking cessation] medication,” and “substituting candy for a cigarette.” During the interview, these participants felt confident that they would quit one day by using many of these strategies. Participants also cited removing tobacco products, pharmacotherapy, and quitting as a team as critical strategies to smoking cessation for justice-involved populations. Consistent with the latest trends on tobacco regulatory and control, CVS Caremark, a leading drugstore chain, is eliminating cigarettes and tobacco products from their store shelves. In addition, pharmacotherapy such as CHANTIX (varenicline) has been clinically proven to assist ’in smoking cessation ( Hoogendoorn, Welsing, & Rutten-van Mölken, 2008 ). One participant (Latina, female, 53 years old, incarcerated for 1.5 years in jail, and released two years prior to this interview), had used Chantix:
I quit actually with Chantix, when I was in the jail. ’Cause we couldn’t smoke in there. I mean, people used to smoke and sneak, and get in trouble and lose their privileges. So when I saw I was heading down the road, I ... asked the doctor in the facility for Chantix. And it worked. ... I stopped smoking for eight months when I was incarcerated.
Another way to achieve smoking cessation was by encouraging former smokers and family members to “quit as a team.” When the interviewer asked, “Is your husband trying to quit when you quit?,” the black female participant, age 48 years, incarcerated in prison for two years, and released two years prior to interview, responded, “Yeah, we want to do it together. We won’t be in the house smoking together.”
Although only a few participants suggested that “quitting as a team” might be helpful to quit smoking, implementing a family or partner team approach in the community and offering a cessation program may improve motivation to decrease or quit smoking and keep former justice-involved participants from reengaging in the habit.
This qualitative study demonstrates that formerly incarcerated men and women released from correctional facilities lack the support from family, peers, and their environment to maintain abstinence from cigarette smoking following release from prison or jail. In fact, the smoking behaviors of family and friends and stressful housing situations and mandated programs emerged from this study as key challenges to maintaining abstinence. Regardless of lengthy abstinence from smoking cigarettes due to incarceration, study participants returned to smoking cigarettes postrelease. Our findings are consistent with those of Bock and colleagues (2013) , who demonstrated that formerly incarcerated individuals have few social models for not smoking and generally lack strong social support from family and particularly from friends relevant to maintaining smoking abstinence after release.
Social factors, specifically homelessness, mandated court or community supervision programs, and a lifetime of exposure to family and friends who are cigarette smokers influence or shape their susceptibility to return to smoking cigarettes. The lack of available smoking cessation strategies to maintain abstinence on release to the community also contributes to relapse. In this study, our interviewees had the fewest resources to withstand societal changes (due to the stresses of living in transitional housing or securing a job, for example), which means that prolonged years in the confines of correctional institutions may have unintended consequences.
The majority of correctional facilities do not offer smoking cessation treatment ( Kauffman, Ferketich, & Wewers, 2008 ). That being said, the relapse rate for smoking is highest the day after release from incarceration, which suggests that offering cessation services, both in correctional facilities and in the transition back to the community, may be critical to reducing tobacco use in this population ( Clarke et al., 2013 ). Although smoking cessation programs are relatively rare in correctional facilities and even infrequent in the community for justice-involved populations, Cropsey et al. (2010) found that more than half of smokers reported that they would be interested in receiving smoking cessation assistance if free help was available. In particular, pharmacotherapy generated a lot of interest; 60 percent of the individuals interested in smoking cessation assistance desired this option ( Cropsey et al., 2010 ).
This interest in smoking cessation is significant because in a previous study Cropsey and Kristeller (2003) found that the stages of change model was a major factor in motivating individuals to quit tobacco use altogether. The “stage of change” concept comes from a five-stage model of change introduced by two substance abuse researchers, Prochaska and DiClemente (1986) . Of the five stages, Cropsey and Kristeller (2003) focused on two: precontemplation and contemplation. Individuals in the precontemplation stage have not yet begun to think about changing their behavior and may not see their substance use as a problem; individuals in the contemplation stage are willing to consider that their use is problematic and that willingness allows them to see possibility for change. Similarly, Thibodeau, Jorenby, Seal, Kim, and Sosman (2010) found that participants who either desired to remain smoke free after release or were uncertain about whether or not they would resume smoking were more likely (82 percent) to remain abstinent for at least the first month outside of a smoke-free prison environment ( Thibodeau et al., 2010 ).
This study also found that daily cigarette smoking varied among participants, ranging from light (four cigarettes) to heavy (60 cigarettes). Half of interviewees were working toward reducing the number of cigarettes smoked daily; unfortunately, none of the participants were involved in a smoking cessation program to support this effort. Given that our sample returned to the community where cigarette smoking is prevalent, smoking cessation interventions tailored to their unique social, cultural, environmental, psychological, and general post-incarceration characteristics may be helpful to treat heavy cigarette use within the subpopulations of racial and ethnic minorities involved in the criminal justice system. In addition, involving family members, partners, and peers in smoking cessation interventions could influence newly released justice-involved men and women not to resume cigarette smoking and possibly maintain long-term abstinence.
Limitations
Although this study provides critical insight into the smoking behavior and causes for cigarette smoking in justice-involved populations, there are several potential limitations that may have affected our findings. For one, although generalizability is not of highest priority in a qualitative study, limiting our study sample to formerly incarcerated men and women from New York correctional facilities who were released to the Bronx County may have affected our ability to extrapolate our results to formerly incarcerated men and women in general. However, given that relatively little has been studied on this topic, we believe that using a purposive sample was justified as an appropriate means to advance knowledge in this area.
Furthermore, although a substantial portion of the criminal justice population does have issues with substance use, limiting our sample to individuals with histories of substance abuse may have excluded an important perspective within this population. However, it can be argued that focusing on individuals with substance use issues is, in fact, more beneficial to our study because it assists us in gathering ideas for a more comprehensive prevention plan that fits the heavy and the light smoker. Focusing on such individuals helps us develop more aggressive smoking cessation strategies tailored toward the more serious substance user; these strategies can later be tailored to fit the needs of formerly incarcerated individuals who do not have serious substance use issues but, nonetheless, need assistance quitting tobacco. Finally, because we used semistructured interviews and self-reporting for data collection, it is possible that social desirability bias could have affected the validity of our results. However, because very few participants (8.3 percent) reported having remained smoke free, it is unlikely that social desirability significantly altered our findings.
In conclusion, our study provided much needed insight into the smoking behaviors and intentions to quit of justice-involved men and women. It is clear that simply forcing these individuals to stop smoking while incarcerated is not enough. Improving access to smoking cessation products such as pharmacotherapy and family- or partner-assisted smoking cessation programs specifically for newly released justice-involved men and women could be vital in addressing cigarette smoking and improving quality of life among a highly vulnerable population.
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Original research article, the association between smoking and health-related quality of life among chinese individuals aged 40 years and older: a cross-sectional study.
- School of Social Development and Public Policy, Beijing Normal University, Beijing, China
Objective: The purpose of this study was to investigate the association between smoking and health-related quality of life among Chinese individuals aged 40 years and older.
Method: Using a stratified multistage sampling method, data from 1,543 adults aged 40 years and older were obtained from a household survey conducted in eight provinces in China. The health-related quality of life was quantified based on the utility index obtained using a standardized instrument entitled “The European Five-Dimensional Health Scale (EQ-5D-5L).” Descriptive statistics were used to summarize the demographic characteristics and social factors of the sample according to smoking status. An instrumental variable (IV) probit model was used to estimate the association between smoking status and health-related quality of life.
Results: Of the 1,543 participants, 485 (31.43%) were smokers and 1,058 (68.57%) were non-smokers. Smoking was negatively associated with the probability of having a higher quality of life ( p < 0.01). For smokers, the average probability of having a higher quality of life was 11.65% lower than when they did not smoke.
Conclusions: These findings suggest that smoking reduces health-related quality of life among Chinese individuals aged 40 years and older. Anti-smoking programs should consider this factor.
Introduction
The tobacco epidemic is the greatest but preventable risk factor for human health. Approximately 8 million people worldwide die from smoking each year, and more than 80% of the 1.3 billion tobacco users worldwide live in low- and middle-income countries ( 1 ). According to the 2018 Global Adult Tobacco Survey (GATS), there are more than 300 million smokers in China, with 52.9% of Chinese male adults smoking ( 2 ). China's health, society, and economy are suffering due to tobacco consumption. More than 1 million people die from tobacco-related deaths in China each year. This number will continue to grow—to ~3 million by 2,050 if China does not act effectively to control its smoking epidemic ( 3 ).
In addition to health threats, smoking can also directly affect health-related quality of life (HRQoL) ( 4 – 7 ). As a comprehensive health evaluation index, quality of life is a self-assessment, that measures people's self-report of their physical state, mental function, social ability, and personal overall condition based on certain socioeconomic and cultural backgrounds and values.
It seems to be a common belief that smoking can help relieve stress and promote relaxation, thus creating the illusion that smokers have a much higher quality of life than non-smokers. However, accumulating evidence suggests that HRQoL is better among non-smokers and former smokers than among current smokers ( 8 – 10 ). The negative association between smoking and HRQoL has been demonstrated in several cross-sectional studies ( 11 – 13 ). Their results were further confirmed by longitudinal studies focusing on the association between smoking status and changes in HRQoL ( 14 – 17 ).
The association between smoking and HRQoL may have different manifestations in different countries, where the cultural context may be at play. Most related studies have been conducted in western countries, including European countries ( 18 – 21 ), the United States ( 22 ), and a few other countries ( 23 – 26 ). Although China is the largest tobacco producer and consumer in the world, few studies have systematically examined the association between smoking and HRQoL among Chinese individuals. Besides, smoking is a continuous behavior, and its process of causing harm to human health is long-term and chronic ( 27 ). As a result, smoking-related side effects may be more easily perceived in middle-aged and older adults than in younger adults. Therefore, while the current mainstream literature shows that smoking is negatively related to quality of life, it is still necessary to evaluate the impact of smoking on the quality of life of the Chinese population aged 40 years and above.
The challenges in studying the association between smoking status and HRQoL are sample self-selection and sampling bias. However, the possible endogeneity of the relationship between smoking status and quality of life has rarely been considered. Whether to smoke is a self-selective behavior that can be influenced by many factors. The omitted variables that may affect both smoking and HRQoL will make the results less credible.
Therefore, the objective of this study was to explore the association between smoking and HRQoL among Chinese individuals aged 40 years and older using an instrumental variable (IV) probit model.
Materials and Methods
Participants.
Participants ( N = 1,543) were Chinese individuals aged 40 years and older recruited in a household survey conducted in China between November 1, 2019, and October 30, 2020. A stratified multistage sampling method was used to select participants from 24 primary health care facilities. These 24 primary health care facilities were selected as follows: firstly, 8 provinces were selected in the east, middle, and west of China: Hebei, Heilongjiang, Shandong, Henan, Hubei, Sichuan, Guizhou, and Shaanxi. Then 2–4 primary health care institutions, including township health centers and community health service centers, were randomly selected in each province. From the areas of 24 primary health care facilities, ~100 households were randomly selected. To be eligible, participants from the 100 households had to live in local communities for at least 6 months, have a minimum age of 40 years, and be willing to participate in this study.
Data Collection
Based on informed consent, the data were collected using anonymous paper and pencil tests. Participants completed questionnaires entitled “Questionnaire on the health of people over 40 years old and its influencing factors.” The validated interviewer-administered questionnaire mainly included (1) general household information, including household type, total household income, and expenditure; (2) basic personal information of household members, including gender, age, and education level; (3) smoking, smoking-related knowledge and chronic diseases of household members; and (4) self-care ability and quality of life of household members.
In this study, the investigators were designated by each investigation unit, and then the investigators were uniformly trained by the research team. The means of household inquiries were adopted by the investigators. Besides, each survey unit identified a contact person who was responsible for survey organization, implementation, quality control, and unified reporting to the subject group. Finally, the research team organized and coded the questionnaires in a unified manner. This study was approved by the ethics committee of the Capital Institute of Pediatrics, Beijing (ID: SHERLL2020017). And the study was conducted following the ethical principles of the Declaration of Helsinki.
Dependent Variables
The dependent variable was “quality of life utility index.” The HRQoL was quantified based on the utility index obtained using a standardized instrument entitled “The European Five-Dimensional Health Scale (EQ-5D-5L).” The EQ-5D is easy to operate and easy to understand by the survey subject and has good reliability and validity. Therefore, it has been widely used in various research fields in many countries ( 28 ) and has become one of the widely used tools for measuring HRQoL. The EQ-5D survey includes five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. An approved Chinese version of the EQ-5D-5L was used, and each level contained five possible responses indicating “no problems,” “slight problems,” “moderate problems,” “severe problems” and “unable to/extreme problems.” If “no problems” were reported for a given level, it was marked as level 1, whereas “unable to/extreme problems” was marked as level 5. The eq5d command in STATA (StataCorp, College Station, Texas, USA) computes an index value from individual responses to the EQ-5D-5L quality-of-life instrument. The EQ-5D index has an upper bound equal to 1 that indicates full health (as evidenced by “no problem” in all domains), whereas 0 represents death ( 29 ). To facilitate analysis, we dichotomized the EQ-5D index according to its mean value.
Independent Variable
The measure of smoking status came from responses to the following question: “Do you smoke now?” In the current study, the concept of smoking was defined as “having smoked at least 1 day in the past 30 days.” Response options were (1) No, (2) Yes, and (3) Have quit smoking. To facilitate statistical analysis, smoking status was dummy coded: (1) smoking: participants who smoke now; (2) non-smoking: participants who had never smoked or had quit smoking.
Based on prior knowledge ( 30 ), covariates included age, gender, educational level, marital status, logarithm of household income, occupation, family size, health status, and province.
Instrumental Variable
Whether the increase in cigarette prices reduced the number of cigarettes smoked (PRS) was used as an instrumental variable.
The variables used in the regression analysis are listed and defined in Table 1 .
Table 1 . The definition and abbreviation of the variables in the model.
Data Analysis
Descriptive analysis.
Descriptive statistics were used to summarize the demographic characteristics and social factors of the sample by smoking status. Characteristics of smokers were compared with those of non-smokers using chi-square tests for categorical variables and Kruskal–Wallis one-way ANOVA tests for continuous variables. Descriptive data are presented as the mean for continuous variables and as percentages for categorical variables.
Effect Estimation
To investigate the association between smoking status and HRQoL, an instrumental variable (IV) probit model was used to control for potential endogeneity problems. Whether the increase in cigarette prices had an effect on the number of cigarettes smoked (PRS) was used as an instrumental variable. It was chosen because it is expected to be correlated with smoking behavior but not directly affect quality of life, thus satisfying the instrumental variable exogeneity requirement. Covariates included age, gender, educational level, marital status, logarithm of annual household income, occupation, family size, health status, and province. The province variable was entered into the model as a dummy variable to give the province a specific intercept to capture sample clustering.
The parameter estimates from the IV probit were further used by marginal analysis to estimate the average treatment effect on the treated (ATET) of smoking on HRQoL. ATET is the estimated average difference of the treatment and control potential outcomes in the treated population. ATET is useful when there is interest in the quantification of the treatment effect in observational studies in which no definite parameter can be used. Therefore, ATET was calculated to obtain more intuitive and practical results.
The instrumental variable (IV) probit model is constructed below as:
Here, Index i refers to the quality of life utility index of the respondents and I n d e x i * represents the latent variable of the quality of life utility index in Equation (1). Smoke i , the independent variable of interest, is binary. β 1 is the coefficient of interest, which provides the estimated effect of smoking on HRQoL. Z is a vector of demographic and socioeconomic variables. I represents the instrumental variable. γ, π 1 and π 2 are the vectors of parameters for the control variables that need to be estimated. μ i and α i are normally distributed error terms in the equation and i denotes an individual respondent.
All statistical analyses were performed using Stata (version 16.0; StataCorp, College Station, Texas, USA). Values of p < 0.05 were considered statistically significant.
Sample Characteristics
The characteristics of subjects are summarized in Table 2 . Of the 1,543 participants, 485 (31.43%) were smokers, and 1,058 (68.57%) were non-smokers. The average age of smokers was 55.51, whereas that of non-smokers was 53.86 ( p = 0.005). The proportion of smokers among males was 54.41%, whereas the proportion among females was 7.76% ( p < 0.001). The mean value of the logarithm of family income for smokers was 11.03 compared to 11.14 for non-smokers ( p = 0.006). There was no significant difference concerning educational status ( p = 0.268), marital status ( p = 0.358), occupations ( p = 0.098), and family size ( p = 0.394) between smokers and non-smokers, whereas there were significant differences in the distribution of provinces ( p < 0.001). Among the participants with two or fewer chronic diseases, 30.91% were smokers and 69.09% were non-smokers ( p = 0.009).
Table 2 . Sample characteristics according to smoking status ( N =1,543).
The Association Between Smoking and HRQoL
The middle column in Table 3 shows the average marginal effects of the probit regression model. The sign of the smoke variable was negative and statistically significant. Smoking decreases the probability of having a higher quality of life by 7.50 percent.
Table 3 . Effect of smoking on HRQoL: IV probit model.
The right column in Table 3 presents the estimated results of the IV probit regression model. As expected, the results show that smoking was negatively correlated with the probability of having a higher quality of life ( p < 0.01). As shown in Table 4 , the estimated ATET of −0.1165 implies that for those who smoked, the average probability of having a higher quality of life would be 11.65 percent lower than it would be if they had not smoked. This result is higher than the 7.50 percent obtained by the probit regression model.
Table 4 . ATET estimates of smoking on the HRQoL: IV probit model.
In Table 3 , from the estimated results of the explanatory variables in the IV probit model, the effects of age and gender on HRQoL were significant at the 0.1 and 1% levels, respectively. The coefficient for age indicates that increases in age lower the probability of having a higher quality of life. If the subject was male, the probability of his having a higher quality of life was greater. Among the province variables, the effects of Shandong, Hebei, and Shaanxi on HRQoL were significant at the 1, 5, and 5% levels, respectively.
Our study indicated that smoking led to a lower probability of having a higher quality of life. For smokers, the average probability of having a higher quality of life was 11.65 percent lower than when they did not smoke.
These compelling findings confirm previous findings reported from other countries such as the UK ( 31 , 32 ), USA ( 22 ), Spain ( 19 , 33 ), Canada ( 4 ) and Turkey ( 8 ). The consistent findings when using different tools to measure HRQoL reinforces the conclusion that smoking is negatively associated with HRQoL ( 11 , 34 , 35 ). According to Toghianifar et al. ( 36 ), smokers scored lower than non-smokers in terms of general health, social functioning, role-emotional and mental health, whereas recent quitters had significantly improved role-emotional and mental health than those who had continued smoking or those who became smokers.
The reasons for the observed negative association between smoking and HRQoL can be attributed to the following aspects. First, smoking increases the risk of non-communicable diseases, including cancers and cardiovascular and respiratory diseases ( 37 ). Adults with more health diseases have worse quality of life ( 26 ). Second, smoking was found to be associated with increased odds of depression ( 38 ) and more clinically significant fatigue ( 39 ). Third, the substances inhaled in cigarettes are related to muscle weakness and decreased vitality ( 40 ). Forth, the EQ-5D used in the present study is a comprehensive measurement of HRQoL in terms of mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. In the long term, smoking would affect the five dimensions of the EQ-5D, thereby reducing HRQoL ( 14 ).
Besides smoking status that was previously discussed, age and gender were found to be independent variables of a lower HRQoL. This finding suggests that smoking intervention programs might be targeted for specific populations, such as men in particular age groups who are current smokers, in order to better improve HRQoL.
Popular belief has it that quitting will decrease HRQoL—because individuals believe it interferes with relationships or produces a loss of smoking related pleasure (such as reducing stress or promoting relaxation). However, the current study indicated that smoking did not improve HRQoL as one would expect. This result contributes to the knowledge of the association between smoking and HRQoL. Knowledge of this association is useful for two reasons: (1) to assist the economic evaluation of cessation programs by providing a more direct measure of health outcomes than the cessation itself; (2) to provide a good reason for individuals to quit smoking.
The strengths of this study are as follows. First, it was based on a large and nationally representative sample of middle-aged and older Chinese individuals. We were able to examine the association between smoking and HRQoL and control many potential confounding factors. The large sample size enabled sufficient power for statistical inference. Second, we used the IV probit regression model to address selection bias. The estimated results of the IV probit regression model were higher than the estimated results of the probit regression model, indicating that the probit model might underestimate the effect of smoking on HRQoL because of selection bias.
There are several limitations to the present study. First, we relied on self-report measures, which may be subject to recall bias and social desirability effects. Second, the study was cross-sectional in design, thus making it hard to obtain any conclusions regarding exact cause-and-effect relationships. Longitudinal data may be needed to further explore the causal relationship between smoking and HRQoL. Third, the generalizability of our results to other populations is limited because we focused on China, and other countries may be different due to ethnic differences.
Findings from the current study suggest that for smokers, the average probability of having a higher quality of life was 11.65% lower than when they did not smoke. Emphasizing that smoking will lead to a lower quality of life may help guide smokers to consciously quit smoking. Therefore, it is necessary for anti-smoking campaigns to clearly point out the negative effect of tobacco use on HRQoL.
Data Availability Statement
The datasets presented in this article are not readily available because data involves personal privacy issues. Requests to access the datasets should be directed to Xi Cheng, 15107100093@163.com.
Author Contributions
XC: conceptualization, data curation, writing - original draft, and writing - review & editing. CJ: conceptualization, funding acquisition, investigation, supervision, and writing - review & editing. All authors contributed to the article and approved the submitted version.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: smoking, health-related quality of life, adult smoking, quality of life, association
Citation: Cheng X and Jin C (2022) The Association Between Smoking and Health-Related Quality of Life Among Chinese Individuals Aged 40 Years and Older: A Cross-Sectional Study. Front. Public Health 10:779789. doi: 10.3389/fpubh.2022.779789
Received: 19 September 2021; Accepted: 28 January 2022; Published: 24 February 2022.
Reviewed by:
Copyright © 2022 Cheng and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Chenggang Jin, cgjin2005@126.com
This article is part of the Research Topic
Health-Related Quality of Life in Health Care
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- 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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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.
Among both the public and the health experts, smoking is recognized as a major behavioral risk factor with a leading attributable health burden worldwide. The health risks of smoking were clearly outlined in a canonical study of disease rates (including lung cancer) and smoking habits in British doctors in 1950 and have been further elaborated in detail over the following seven decades 1 , 2 . In 2005, evidence of the health consequences of smoking galvanized the adoption of the first World Health Organization (WHO) treaty, the Framework Convention on Tobacco Control, in an attempt to drive reductions in global tobacco use and second-hand smoke exposure 3 . However, as of 2020, an estimated 1.18 billion individuals globally were current smokers and 7 million deaths and 177 million disability-adjusted life-years were attributed to smoking, reflecting a persistent public health challenge 4 . Quantifying the relationship between smoking and various important health outcomes—in particular, highlighting any significant dose–response relationships—is crucial to understanding the attributable health risk experienced by these individuals and informing responsive public policy.
Existing literature on the relationship between smoking and specific health outcomes is prolific, including meta-analyses, cohort studies and case–control studies analyzing the risk of outcomes such as lung cancer 5 , 6 , 7 , chronic obstructive pulmonary disease (COPD) 8 , 9 , 10 and ischemic heart disease 11 , 12 , 13 , 14 due to smoking. There are few if any attempts, however, to systematically and comprehensively evaluate the landscape of evidence on smoking risk across a diverse range of health outcomes, with most current research focusing on risk or attributable burden of smoking for a specific condition 7 , 15 , thereby missing the opportunity to provide a comprehensive picture of the health risk experienced by smokers. Furthermore, although evidence surrounding specific health outcomes, such as lung cancer, has generated widespread consensus, findings about the attributable risk of other outcomes are much more heterogeneous and inconclusive 16 , 17 , 18 . These studies also vary in their risk definitions, with many comparing dichotomous exposure measures of ever smokers versus nonsmokers 19 , 20 . Others examine the distinct risks of current smokers and former smokers compared with never smokers 21 , 22 , 23 . Among the studies that do analyze dose–response relationships, there is large variation in the units and dose categories used in reporting their findings (for example, the use of pack-years or cigarettes per day) 24 , 25 , which complicates the comparability and consolidation of evidence. This, in turn, can obscure data that could inform personal health choices, public health practices and policy measures. Guidance on the health risks of smoking, such as the Surgeon General’s Reports on smoking 26 , 27 , is often based on experts’ evaluation of heterogenous evidence, which, although extremely useful and well suited to carefully consider nuances in the evidence, is fundamentally subjective.
The present study, as part of the Global Burden of Diseases, Risk Factors, and Injuries Study (GBD) 2020, re-estimated the continuous dose–response relationships (the mean risk functions and associated uncertainty estimates) between current smoking and 36 health outcomes (Supplementary Table 1 ) by identifying input studies using a systematic review approach and employing a meta-analytic method 28 . The 36 health outcomes that were selected based on existing evidence of a relationship included 16 cancers (lung cancer, esophageal cancer, stomach cancer, leukemia, liver cancer, laryngeal cancer, breast cancer, cervical cancer, colorectal cancer, lip and oral cavity cancer, nasopharyngeal cancer, other pharynx cancer (excluding nasopharynx cancer), pancreatic cancer, bladder cancer, kidney cancer and prostate cancer), 5 cardiovascular diseases (CVDs: ischemic heart disease, stroke, atrial fibrillation and flutter, aortic aneurysm and peripheral artery disease) and 15 other diseases (COPD, lower respiratory tract infections, tuberculosis, asthma, type 2 diabetes, Alzheimer’s disease and related dementias, Parkinson’s disease, multiple sclerosis, cataracts, gallbladder diseases, low back pain, peptic ulcer disease, rheumatoid arthritis, macular degeneration and fractures). Definitions of the outcomes are described in Supplementary Table 1 . We conducted a separate systematic review for each risk–outcome pair with the exception of cancers, which were done together in a single systematic review. This approach allowed us to systematically identify all relevant studies indexed in PubMed up to 31 May 2022, and we extracted relevant data on risk of smoking, including study characteristics, following a pre-specified template (Supplementary Table 2 ). The meta-analytic tool overcomes many of the limitations of traditional meta-analyses by incorporating between-study heterogeneity into the uncertainty of risk estimates, accounting for small numbers of studies, relaxing the assumption of log(linearity) applied to the risk functions, handling differences in exposure ranges between comparison groups, and systematically testing and adjusting for bias due to study designs and characteristics. We then estimated the burden-of-proof risk function (BPRF) for each risk–outcome pair, as proposed by Zheng et al. 29 ; the BPRF is a conservative risk function defined as the 5th quantile curve (for harmful risks) that reflects the smallest harmful effect at each level of exposure consistent with the available evidence. Given all available data for each outcome, the risk of smoking is at least as harmful as the BPRF indicates.
We used the BPRF for each risk–outcome pair to calculate risk–outcome scores (ROSs) and categorize the strength of evidence for the association between smoking and each health outcome using a star rating from 1 to 5. The interpretation of the star ratings is as follows: 1 star (*) indicates no evidence of association; 2 stars (**) correspond to a 0–15% increase in risk across average range of exposures for harmful risks; 3 stars (***) represent a 15–50% increase in risk; 4 stars (****) refer to >50–85% increase in risk; and 5 stars (*****) equal >85% increase in risk. The thresholds for each star rating were developed in consultation with collaborators and other stakeholders.
The increasing disease burden attributable to current smoking, particularly in low- and middle-income countries 4 , demonstrates the relevance of the present study, which quantifies the strength of the evidence using an objective, quantitative, comprehensive and comparative framework. Findings from the present study can be used to support policy makers in making informed smoking recommendations and regulations focusing on the associations for which the evidence is strongest (that is, the 4- and 5-star associations). However, associations with a lower star rating cannot be ignored, especially when the outcome has high prevalence or severity. A summary of the main findings, limitations and policy implications of the study is presented in Table 1 .
We evaluated the mean risk functions and the BPRFs for 36 health outcomes that are associated with current smoking 30 (Table 2 ). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 31 for each of our systematic reviews, we identified studies reporting relative risk (RR) of incidence or mortality from each of the 36 selected outcomes for smokers compared with nonsmokers. We reviewed 21,108 records, which were identified to have been published between 1 May 2018 and 31 May 2022; this represents the most recent time period since the last systematic review of the available evidence for the GBD at the time of publication. The meta-analyses reported in the present study for each of the 36 health outcomes are based on evidence from a total of 793 studies published between 1970 and 2022 (Extended Data Fig. 1 – 5 and Supplementary Information 1.5 show the PRISMA diagrams for each outcome). Only prospective cohort and case–control studies were included for estimating dose–response risk curves, but cross-sectional studies were also included for estimating the age pattern of smoking risk on cardiovascular and circulatory disease (CVD) outcomes. Details on each, including the study’s design, data sources, number of participants, length of follow-up, confounders adjusted for in the input data and bias covariates included in the dose–response risk model, can be found in Supplementary Information 2 and 3 . The theoretical minimum risk exposure level used for current smoking was never smoking or zero 30 .
Five-star associations
When the most conservative interpretation of the evidence, that is, the BPRF, suggests that the average exposure (15th–85th percentiles of exposure) of smoking increases the risk of a health outcome by >85% (that is, ROS > 0.62), smoking and that outcome are categorized as a 5-star pair. Among the 36 outcomes, there are 5 that have a 5-star association with current smoking: laryngeal cancer (375% increase in risk based on the BPRF, 1.56 ROS), aortic aneurysm (150%, 0.92), peripheral artery disease (137%, 0.86), lung cancer (107%, 0.73) and other pharynx cancer (excluding nasopharynx cancer) (92%, 0.65).
Results for all 5-star risk–outcome pairs are available in Table 2 and Supplementary Information 4.1 . In the present study, we provide detailed results for one example 5-star association: current smoking and lung cancer. We extracted 371 observations from 25 prospective cohort studies and 53 case–control studies across 25 locations (Supplementary Table 3 ) 5 , 6 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 . Exposure ranged from 1 pack-year to >112 pack-years, with the 85th percentile of exposure being 50.88 pack-years (Fig. 1a ).
a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on the x axis and the estimated s.d. that includes reported s.d. and between-study heterogeneity on the y axis.
We found a very strong and significant harmful relationship between pack-years of current smoking and the RR of lung cancer (Fig. 1b ). The mean RR of lung cancer at 20 pack-years of smoking was 5.11 (95% uncertainty interval (UI) inclusive of between-study heterogeneity = 1.84–14.99). At 50.88 pack-years (85th percentile of exposure), the mean RR of lung cancer was 13.42 (2.63–74.59). See Table 2 for mean RRs at other exposure levels. The BPRF, which represents the most conservative interpretation of the evidence (Fig. 1a ), suggests that smoking in the 15th–85th percentiles of exposure increases the risk of lung cancer by an average of 107%, yielding an ROS of 0.73.
The relationship between pack-years of current smoking and RR of lung cancer is nonlinear, with diminishing impact of further pack-years of smoking, particularly for middle-to-high exposure levels (Fig. 1b ). To reduce the effect of bias, we adjusted observations that did not account for more than five confounders, including age and sex, because they were the significant bias covariates identified by the bias covariate selection algorithm 29 (Supplementary Table 7 ). The reported RRs across studies were very heterogeneous. Our meta-analytic method, which accounts for the reported uncertainty in both the data and between-study heterogeneity, fit the data and covered the estimated residuals well (Fig. 1c ). After trimming 10% of outliers, we still detected publication bias in the results for lung cancer. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 5-star pairs.
Four-star associations
When the BPRF suggests that the average exposure of smoking increases the risk of a health outcome by 50–85% (that is, ROS > 0.41–0.62), smoking is categorized as having a 4-star association with that outcome. We identified three outcomes with a 4-star association with smoking: COPD (72% increase in risk based on the BPRF, 0.54 ROS), lower respiratory tract infection (54%, 0.43) and pancreatic cancer (52%, 0.42).
In the present study, we provide detailed results for one example 4-star association: current smoking and COPD. We extracted 51 observations from 11 prospective cohort studies and 4 case–control studies across 36 locations (Supplementary Table 3 ) 6 , 8 , 9 , 10 , 78 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 . Exposure ranged from 1 pack-year to 100 pack-years, with the 85th percentile of exposure in the exposed group being 49.75 pack-years.
We found a strong and significant harmful relationship between pack-years of current smoking and RR of COPD (Fig. 2b ). The mean RR of COPD at 20 pack-years was 3.17 (1.60–6.55; Table 2 reports RRs at other exposure levels). At the 85th percentile of exposure, the mean RR of COPD was 6.01 (2.08–18.58). The BPRF suggests that average smoking exposure raises the risk of COPD by an average of 72%, yielding an ROS of 0.54. The results for the other health outcomes that have an association with smoking rated as 4 stars are shown in Table 2 and Supplementary Information 4.2 .
a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on th e x axis and the estimated s.d. that includes the reported s.d. and between-study heterogeneity on the y axis.
The relationship between smoking and COPD is nonlinear, with diminishing impact of further pack-years of current smoking on risk of COPD, particularly for middle-to-high exposure levels (Fig. 2a ). To reduce the effect of bias, we adjusted observations that did not account for age and sex and/or were generated for individuals aged >65 years 116 , because they were the two significant bias covariates identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was large heterogeneity in the reported RRs across studies, and our meta-analytic method fit the data and covered the estimated residuals well (Fig. 2b ). Although we trimmed 10% of outliers, publication bias was still detected in the results for COPD. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for reported RR data and alternative exposures across studies for the remaining health outcomes that have a 4-star association with smoking.
Three-star associations
When the BPRF suggests that the average exposure of smoking increases the risk of a health outcome by 15–50% (or, when protective, decreases the risk of an outcome by 13–34%; that is, ROS >0.14–0.41), the association between smoking and that outcome is categorized as having a 3-star rating. We identified 15 outcomes with a 3-star association: bladder cancer (40% increase in risk, 0.34 ROS); tuberculosis (31%, 0.27); esophageal cancer (29%, 0.26); cervical cancer, multiple sclerosis and rheumatoid arthritis (each 23–24%, 0.21); lower back pain (22%, 0.20); ischemic heart disease (20%, 0.19); peptic ulcer and macular degeneration (each 19–20%, 0.18); Parkinson's disease (protective risk, 15% decrease in risk, 0.16); and stomach cancer, stroke, type 2 diabetes and cataracts (each 15–17%, 0.14–0.16).
We present the findings on smoking and type 2 diabetes as an example of a 3-star risk association. We extracted 102 observations from 24 prospective cohort studies and 4 case–control studies across 15 locations (Supplementary Table 3 ) 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 . The exposure ranged from 1 cigarette to 60 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 26.25 cigarettes smoked per day.
We found a moderate and significant harmful relationship between cigarettes smoked per day and the RR of type 2 diabetes (Fig. 3b ). The mean RR of type 2 diabetes at 20 cigarettes smoked per day was 1.49 (1.18–1.90; see Table 2 for other exposure levels). At the 85th percentile of exposure, the mean RR of type 2 diabetes was 1.54 (1.20–2.01). The BPRF suggests that average smoking exposure raises the risk of type 2 diabetes by an average of 16%, yielding an ROS of 0.15. See Table 2 and Supplementary Information 4.3 for results for the additional health outcomes with an association with smoking rated as 3 stars.
a , The log(RR) function. b , RR function. c , A modified funnel plot showing the residuals (relative to 0) on the x axis and the estimated s.d. that includes the reported s.d. and between-study heterogeneity on the y axis.
The relationship between smoking and type 2 diabetes is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Fig. 3a ). We adjusted observations that were generated in subpopulations, because it was the only significant bias covariate identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was moderate heterogeneity in the observed RR data across studies and our meta-analytic method fit the data and covered the estimated residuals extremely well (Fig. 3b,c ). After trimming 10% of outliers, we still detected publication bias in the results for type 2 diabetes. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 3-star pairs.
Two-star associations
When the BPRF suggests that the average exposure of smoking increases the risk of an outcome by 0–15% (that is, ROS 0.0–0.14), the association between smoking and that outcome is categorized as a 2-star rating. We identified six 2-star outcomes: nasopharyngeal cancer (14% increase in risk, 0.13 ROS); Alzheimer’s and other dementia (10%, 0.09); gallbladder diseases and atrial fibrillation and flutter (each 6%, 0.06); lip and oral cavity cancer (5%, 0.05); and breast cancer (4%, 0.04).
We present the findings on smoking and breast cancer as an example of a 2-star association. We extracted 93 observations from 14 prospective cohort studies and 9 case–control studies across 14 locations (Supplementary Table 3 ) 84 , 87 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 . The exposure ranged from 1 cigarette to >76 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 34.10 cigarettes smoked per day.
We found a weak but significant relationship between pack-years of current smoking and RR of breast cancer (Extended Data Fig. 6 ). The mean RR of breast cancer at 20 pack-years was 1.17 (1.04–1.31; Table 2 reports other exposure levels). The BPRF suggests that average smoking exposure raises the risk of breast cancer by an average of 4%, yielding an ROS of 0.04. See Table 2 and Supplementary Information 4.4 for results on the additional health outcomes for which the association with smoking has been categorized as 2 stars.
The relationship between smoking and breast cancer is nonlinear, particularly for high exposure levels where the mean risk curve becomes flat (Extended Data Fig. 6a ). To reduce the effect of bias, we adjusted observations that were generated in subpopulations, because it was the only significant bias covariate identified by the bias covariate selection algorithm (Supplementary Table 7 ). There was heterogeneity in the reported RRs across studies, but our meta-analytic method fit the data and covered the estimated residuals (Extended Data Fig. 6b ). After trimming 10% of outliers, we did not detect publication bias in the results for breast cancer. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 2-star pairs.
One-star associations
When average exposure to smoking does not significantly increase (or decrease) the risk of an outcome, once between-study heterogeneity and other sources of uncertainty are accounted for (that is, ROS < 0), the association between smoking and that outcome is categorized as 1 star, indicating that there is not sufficient evidence for the effect of smoking on the outcome to reject the null (that is, there may be no association). There were seven outcomes with an association with smoking that rated as 1 star: colorectal and kidney cancer (each –0.01 ROS); leukemia (−0.04); fractures (−0.05); prostate cancer (−0.06); liver cancer (−0.32); and asthma (−0.64).
We use smoking and prostate cancer as examples of a 1-star association. We extracted 78 observations from 21 prospective cohort studies and 1 nested case–control study across 15 locations (Supplementary Table 3 ) 157 , 160 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 . The exposure among the exposed group ranged from 1 cigarette to 90 cigarettes smoked per day, with the 85th percentile of exposure in the exposed group being 29.73 cigarettes smoked per day.
Based on our conservative interpretation of the data, we did not find a significant relationship between cigarettes smoked per day and the RR of prostate cancer (Fig. 4B ). The exposure-averaged BPRF for prostate cancer was 0.94, which was opposite null from the full range of mean RRs, such as 1.16 (0.89–1.53) at 20 cigarettes smoked per day. The corresponding ROS was −0.06, which is consistent with no evidence of an association between smoking and increased risk of prostate cancer. See Table 2 and Supplementary Information 4.5 for results for the additional outcomes that have a 1-star association with smoking.
The relationship between smoking and prostate cancer is nonlinear, particularly for middle-to-high exposure levels where the mean risk curve becomes flat (Fig. 4a ). We did not adjust for any bias covariate because no significant bias covariates were selected by the algorithm (Supplementary Table 7 ). The RRs reported across studies were very heterogeneous, but our meta-analytic method fit the data and covered the estimated residuals well (Fig. 4b,c ). The ROS associated with the BPRF is −0.05, suggesting that the most conservative interpretation of all evidence, after accounting for between-study heterogeneity, indicates an inconclusive relationship between smoking exposure and the risk of prostate cancer. After trimming 10% of outliers, we still detected publication bias in the results for prostate cancer, which warrants further studies using sample populations. See Supplementary Tables 4 and 7 for study bias characteristics and selected bias covariates, Supplementary Fig. 5 for results without 10% trimming and Supplementary Table 8 for observed RR data and alternative exposures across studies for the remaining 1-star pairs.
Age-specific dose–response risk for CVD outcomes
We produced age-specific dose–response risk curves for the five selected CVD outcomes ( Methods ). The ROS associated with each smoking–CVD pair was calculated based on the reference risk curve estimated using all risk data regardless of age information. Estimation of the BPRF, calculation of the associated ROS and star rating of the smoking–CVD pairs follow the same rules as the other non-CVD smoking–outcome pairs (Table 1 and Supplementary Figs. 2 – 4 ). Once we had estimated the reference dose–response risk curve for each CVD outcome, we determined the age group of the reference risk curve. The reference age group is 55–59 years for all CVD outcomes, except for peripheral artery disease, the reference age group for which is 60–64 years. We then estimated the age pattern of smoking on all CVD outcomes (Supplementary Fig. 2 ) and calculated age attenuation factors of the risk for each age group by comparing the risk of each age group with that of the reference age group, using the estimated age pattern (Supplementary Fig. 3 ). Last, we applied the draws of age attenuation factors of each age group to the dose–response risk curve for the reference age group to produce the age group-specific dose–response risk curves for each CVD outcome (Supplementary Fig. 4 ).
Using our burden-of-proof meta-analytic methods, we re-estimated the dose–response risk of smoking on 36 health outcomes that had previously been demonstrated to be associated with smoking 30 , 186 . Using these methods, which account for both the reported uncertainty of the data and the between-study heterogeneity, we found that 29 of the 36 smoking–outcome pairs are supported by evidence that suggests a significant dose–response relationship between smoking and the given outcome (28 with a harmful association and 1 with a protective association). Conversely, after accounting for between-study heterogeneity, the available evidence of smoking risk on seven outcomes (that is, colon and rectum cancer, kidney cancer, leukemia, prostate cancer, fractures, liver cancer and asthma) was insufficient to reject the null or draw definitive conclusions on their relationship to smoking. Among the 29 outcomes that have evidence supporting a significant relationship to smoking, 8 had strong-to-very-strong evidence of a relationship, meaning that, given all the available data on smoking risk, we estimate that average exposure to smoking increases the risk of those outcomes by >50% (4- and 5-star outcomes). The currently available evidence for the remaining 21 outcomes with a significant association with current smoking was weak to moderate, indicating that smoking increases the risk of those outcomes by at least >0–50% (2- and 3-star associations).
Even under our conservative interpretation of the data, smoking is irrefutably harmful to human health, with the greatest increases in risk occurring for laryngeal cancer, aortic aneurysm, peripheral artery disease, lung cancer and other pharynx cancer (excluding nasopharynx cancer), which collectively represent large causes of death and ill-health. The magnitude of and evidence for the associations between smoking and its leading health outcomes are among the highest currently analyzed in the burden-of-proof framework 29 . The star ratings assigned to each smoking–outcome pair offer policy makers a way of categorizing and comparing the evidence for a relationship between smoking and its potential health outcomes ( https://vizhub.healthdata.org/burden-of-proof ). We found that, for seven outcomes in our analysis, there was insufficient or inconsistent evidence to demonstrate a significant association with smoking. This is a key finding because it demonstrates the need for more high-quality data for these particular outcomes; availability of more data should improve the strength of evidence for whether or not there is an association between smoking and these health outcomes.
Our systematic review approach and meta-analytic methods have numerous benefits over existing systematic reviews and meta-analyses on the same topic that use traditional random effects models. First, our approach relaxes the log(linear) assumption, using a spline ensemble to estimate the risk 29 . Second, our approach allows variable reference groups and exposure ranges, allowing for more accurate estimates regardless of whether or not the underlying relative risk is log(linear). Furthermore, it can detect outliers in the data automatically. Finally, it quantifies uncertainty due to between-study heterogeneity while accounting for small numbers of studies, minimizing the risk that conclusions will be drawn based on spurious findings.
We believe that the results for the association between smoking and each of the 36 health outcomes generated by the present study, including the mean risk function, BPRF, ROS, average excess risk and star rating, could be useful to a range of stakeholders. Policy makers can formulate their decisions on smoking control priorities and resource allocation based on the magnitude of the effect and the consistency of the evidence relating smoking to each of the 36 outcomes, as represented by the ROS and star rating for each smoking–outcome association 187 . Physicians and public health practitioners can use the estimates of average increased risk and the star rating to educate patients and the general public about the risk of smoking and to promote smoking cessation 188 . Researchers can use the estimated mean risk function or BPRF to obtain the risk of an outcome at a given smoking exposure level, as well as uncertainty surrounding that estimate of risk. The results can also be used in the estimation of risk-attributable burden, that is, the deaths and disability-adjusted life-years due to each outcome that are attributable to smoking 30 , 186 . For the general public, these results could help them to better understand the risk of smoking and manage their health 189 .
Although our meta-analysis was comprehensive and carefully conducted, there are limitations to acknowledge. First, the bias covariates used, although carefully extracted and evaluated, were based on observable study characteristics and thus may not fully capture unobserved characteristics such as study quality or context, which might be major sources of bias. Second, if multiple risk estimates with different adjustment levels were reported in a given study, we included only the fully adjusted risk estimate and modeled the adjustment level according to the number of covariates adjusted for (rather than which covariates were adjusted for) and whether a standard adjustment for age and sex had been applied. This approach limited our ability to make full use of all available risk estimates in the literature. Third, although we evaluated the potential for publication bias in the data, we did not test for other forms of bias such as when studies are more consistent with each other than expected by chance 29 . Fourth, our analysis assumes that the relationships between smoking and health outcomes are similar across geographical regions and over time. We do not have sufficient evidence to quantify how the relationships may have evolved over time because the composition of smoking products has also changed over time. Perhaps some of the heterogeneity of the effect sizes in published studies reflects this; however, this cannot be discerned with the currently available information.
In the future, we plan to include crude and partially adjusted risk estimates in our analyses to fully incorporate all available risk estimates, to model the adjusted covariates in a more comprehensive way by mapping the adjusted covariates across all studies comprehensively and systematically, and to develop methods to evaluate additional forms of potential bias. We plan to update our results on a regular basis to provide timely and up-to-date evidence to stakeholders.
To conclude, we have re-estimated the dose–response risk of smoking on 36 health outcomes while synthesizing all the available evidence up to 31 May 2022. We found that, even after factoring in the heterogeneity between studies and other sources of uncertainty, smoking has a strong-to-very-strong association with a range of health outcomes and confirmed that smoking is irrefutably highly harmful to human health. We found that, due to small numbers of studies, inconsistency in the data, small effect sizes or a combination of these reasons, seven outcomes for which some previous research had found an association with smoking did not—under our meta-analytic framework and conservative approach to interpreting the data—have evidence of an association. Our estimates of the evidence for risk of smoking on 36 selected health outcomes have the potential to inform the many stakeholders of smoking control, including policy makers, researchers, public health professionals, physicians, smokers and the general public.
For the present study, we used a meta-analytic tool, MR-BRT (metaregression—Bayesian, regularized, trimmed), to estimate the dose–response risk curves of the risk of a health outcome across the range of current smoking levels along with uncertainty estimates 28 . Compared with traditional meta-analysis using linear mixed effect models, MR-BRT relaxes the assumption of a log(linear) relationship between exposure and risk, incorporates between-study heterogeneity into the uncertainty of risk estimates, handles estimates reported across different exposure categories, automatically identifies and trims outliers, and systematically tests and adjusts for bias due to study designs and characteristics. The meta-analytic methods employed by the present study followed the six main steps proposed by Zheng et al. 28 , 29 , namely: (1) enacting a systematic review approach and data extraction following a pre-specified and standardized protocol; (2) estimating the shape of the relationship between exposure and RR; (3) evaluating and adjusting for systematic bias as a function of study characteristics and risk estimation; (4) quantifying between-study heterogeneity while adjusting for within-study correlation and the number of studies; (5) evaluating potential publication or reporting biases; and (6) estimating the mean risk function and the BPRF, calculating the ROS and categorizing smoking–outcome pairs using a star-rating scheme from 1 to 5.
The estimates for our primary indicators of this work—mean RRs across a range of exposures, BRPFs, ROSs and star ratings for each risk–outcome pair—are not specific to or disaggregated by specific populations. We did not estimate RRs separately for different locations, sexes (although the RR of prostate cancer was estimated only for males and of cervical and breast cancer only for females) or age groups (although this analysis was applied to disease endpoints in adults aged ≥30 years only and, as detailed below, age-specific estimates were produced for the five CVD outcomes).
The present study complies with the PRISMA guidelines 190 (Supplementary Tables 9 and 10 and Supplementary Information 1.5 ) and Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations 191 (Supplementary Table 11 ). The study was approved by the University of Washington Institutional Review Board (study no. 9060). The systematic review approach was not registered.
Selecting health outcomes
In the present study, current smoking is defined as the current use of any smoked tobacco product on a daily or occasional basis. Health outcomes were initially selected using the World Cancer Research Fund criteria for convincing or probable evidence as described in Murray et al. 186 . The 36 health outcomes that were selected based on existing evidence of a relationship included 16 cancers (lung cancer, esophageal cancer, stomach cancer, leukemia, liver cancer, laryngeal cancer, breast cancer, cervical cancer, colorectal cancer, lip and oral cavity cancer, nasopharyngeal cancer, other pharynx cancer (excluding nasopharynx cancer), pancreatic cancer, bladder cancer, kidney cancer and prostate cancer), 5 CVDs (ischemic heart disease, stroke, atrial fibrillation and flutter, aortic aneurysm and peripheral artery disease) and 15 other diseases (COPD, lower respiratory tract infections, tuberculosis, asthma, type 2 diabetes, Alzheimer’s disease and related dementias, Parkinson’s disease, multiple sclerosis, cataracts, gallbladder diseases, low back pain, peptic ulcer disease, rheumatoid arthritis, macular degeneration and fracture). Definitions of the outcomes are described in Supplementary Table 1 .
Step 1: systematic review approach to literature search and data extraction
Informed by the systematic review approach we took for the GBD 2019 (ref. 30 ), for the present study we identified input studies in the literature using a systematic review approach for all 36 smoking–outcome pairs using updated search strings to identify all relevant studies indexed in PubMed up to 31 May 2022 and extracted data on smoking risk estimates. Briefly, the studies that were extracted represented several types of study design (for example, cohort and case–control studies), measured exposure in several different ways and varied in their choice of reference categories (where some compared current smokers with never smokers, whereas others compared current smokers with nonsmokers or former smokers). All these study characteristics were catalogued systematically and taken into consideration during the modeling part of the analysis.
In addition, for CVD outcomes, we also estimated the age pattern of risk associated with smoking. We applied a systematic review of literature approach for smoking risk for the five CVD outcomes. We developed a search string to search for studies reporting any association between binary smoking status (that is, current, former and ever smokers) and the five CVD outcomes from 1 January 1970 to 31 May 2022, and included only studies reporting age-specific risk (RR, odds ratio (OR), hazard ratio (HR)) of smoking status. The inclusion criteria and results of the systematic review approach are reported in accordance with PRISMA guidelines 31 . Details for each outcome on the search string used in the systematic review approach, refined inclusion and exclusion criteria, data extraction template and PRISMA diagram are given in Supplementary Information 1 . Title and/or abstract screening, full text screening and data extraction were conducted by 14 members of the research team and extracted data underwent manual quality assurance by the research team to verify accuracy.
Selecting exposure categories
Cumulative exposure in pack-years was the measure of exposure used for COPD and all cancer outcomes except for prostate cancer, to reflect the risk of both duration and intensity of current smoking on these outcomes. For prostate cancer, CVDs and all the other outcomes except for fractures, we used cigarette-equivalents smoked per day as the exposure for current smoking, because smoking intensity is generally thought to be more important than duration for these outcomes. For fractures, we used binary exposure, because there were few studies examining intensity or duration of smoking on fractures. The smoking–outcome pairs and the corresponding exposures are summarized in Supplementary Table 4 and are congruent with the GBD 2019 (refs. 30 , 186 ).
Steps 2–5: modeling dose–response RR of smoking on the selected health outcomes
Of the six steps proposed by Zheng et al. 29 , steps 2–5 cover the process of modeling dose–response risk curves. In step 2, we estimated the shape (or the ‘signal’) of the dose–response risk curves, integrating over different exposure ranges. To relax the log(linear) assumption usually applied to continuous dose–response risk and make the estimates robust to the placement of spline knots, we used an ensemble spline approach to fit the functional form of the dose–response relationship. The final ensemble model was a weighted combination of 50 models with random knot placement, with the weight of each model proportional to measures of model fit and total variation. To avoid the influence of extreme data and reduce publication bias, we trimmed 10% of data for each outcome as outliers. We also applied a monotonicity constraint to ensure that the mean risk curves were nondecreasing (or nonincreasing in the case of Parkinson’s disease).
In step 3, following the GRADE approach 192 , 193 , we quantified risk of bias across six domains, namely, representativeness of the study population, exposure, outcome, reverse causation, control for confounding and selection bias. Details about the bias covariates are provided in Supplementary Table 4 . We systematically tested for the effect of bias covariates using metaregression, selected significant bias covariates using the Lasso approach 194 , 195 and adjusted for the selected bias covariates in the final risk curve.
In step 4, we quantified between-study heterogeneity accounting for within-study correlation, uncertainty of the heterogeneity, as well as small number of studies. Specifically, we used a random intercept in the mixed-effects model to account for the within-study correlation and used a study-specific random slope with respect to the ‘signal’ to capture between-study heterogeneity. As between-study heterogeneity can be underestimated or even zero when the number of studies is small 196 , 197 , we used Fisher’s information matrix to estimate the uncertainty of the heterogeneity 198 and incorporated that uncertainty into the final results.
In step 5, in addition to generating funnel plots and visually inspecting for asymmetry (Figs. 1c , 2c , 3c and 4c and Extended Data Fig. 6c ) to identify potential publication bias, we also statistically tested for potential publication or reporting bias using Egger’s regression 199 . We flagged potential publication bias in the data but did not correct for it, which is in line with the general literature 10 , 200 , 201 . Full details about the modeling process have been published elsewhere 29 and model specifications for each outcome are in Supplementary Table 6 .
Step 6: estimating the mean risk function and the BPRF
In the final step, step 6, the metaregression model inclusive of the selected bias covariates from step 3 (for example, the highest adjustment level) was used to predict the mean risk function and its 95% UI, which incorporated the uncertainty of the mean effect, between-study heterogeneity and the uncertainty in the heterogeneity estimate accounting for small numbers of studies. Specifically, 1,000 draws were created for each 0.1 level of doses from 0 pack-years to 100 pack-years or cigarette-equivalents smoked per day using the Bayesian metaregression model. The mean of the 1,000 draws was used to estimate the mean risk at each exposure level, and the 25th and 95th draws were used to estimate the 95% UIs for the mean risk at each exposure level.
The BPRF 29 is a conservative estimate of risk function consistent with the available evidence, correcting for both between-study heterogeneity and systemic biases related to study characteristics. The BPRF is defined as either the 5th (if harmful) or 95th (if protective) quantile curve closest to the line of log(RR) of 0, which defines the null (Figs. 1a , 2b , 3a and 4a ). The BPRF represents the smallest harmful (or protective) effect of smoking on the corresponding outcome at each level of exposure that is consistent with the available evidence. A BPRF opposite null from the mean risk function indicates that insufficient evidence is available to reject null, that is, that there may not be an association between risk and outcome. Likewise, the further the BPRF is from null on the same side of null as the mean risk function, the higher the magnitude and evidence for the relationship. The BPRF can be interpreted as indicating that, even accounting for between-study heterogeneity and its uncertainty, the log(RR) across the studied smoking range is at least as high as the BPRF (or at least as low as the BPRF for a protective risk).
To quantify the strength of the evidence, we calculated the ROS for each smoking–outcome association as the signed value of the log(BPRF) averaged between the 15th and 85th percentiles of observed exposure levels for each outcome. The ROS is a single summary of the effect of smoking on the outcome, with higher positive ROSs corresponding to stronger and more consistent evidence and a higher average effect size of smoking and a negative ROS, suggesting that, based on the available evidence, there is no significant effect of smoking on the outcome after accounting for between-study heterogeneity.
For ease of communication, we further classified each smoking–outcome association into a star rating from 1 to 5. Briefly, 1-star associations have an ROS <0, indicating that there is insufficient evidence to find a significant association between smoking and the selected outcome. We divided the positive ROSs into ranges 0.0–0.14 (2-star), >0.14–0.41 (3-star), >0.41–0.62 (4-star) and >0.62 (5-star). These categories correspond to excess risk ranges for harmful risks of 0–15%, >15–50%, >50–85% and >85%. For protective risks, the ranges of exposure-averaged decreases in risk by star rating are 0–13% (2 stars), >13–34% (3 stars), >34–46% (4 stars) and >46% (5 stars).
Among the 36 smoking–outcome pairs analyzed, smoking fracture was the only binary risk–outcome pair, which was due to limited data on the dose–response risk of smoking on fracture 202 . The estimation of binary risk was simplified because the RR was merely a comparison between current smokers and nonsmokers or never smokers. The concept of ROS for continuous risk can naturally extend to binary risk because the BPRF is still defined as the 5th percentile of the effect size accounting for data uncertainty and between-study heterogeneity. However, binary ROSs must be divided by 2 to make them comparable with continuous ROSs, which were calculated by averaging the risk over the range between the 15th and the 85th percentiles of observed exposure levels. Full details about estimating mean risk functions, BPRFs and ROSs for both continuous and binary risk–outcome pairs can be found elsewhere 29 .
Estimating the age-specific risk function for CVD outcomes
For non-CVD outcomes, we assumed that the risk function was the same for all ages and all sexes, except for breast, cervical and prostate cancer, which were assumed to apply only to females or males, respectively. As the risk of smoking on CVD outcomes is known to attenuate with increasing age 203 , 204 , 205 , 206 , we adopted a four-step approach for GBD 2020 to produce age-specific dose–response risk curves for CVD outcomes.
First, we estimated the reference dose–response risk of smoking for each CVD outcome using dose-specific RR data for each outcome regardless of the age group information. This step was identical to that implemented for the other non-CVD outcomes. Once we had generated the reference curve, we determined the age group associated with it by calculating the weighted mean age across all dose-specific RR data (weighted by the reciprocal of the s.e.m. of each datum). For example, if the weighted mean age of all dose-specific RR data was 56.5, we estimated the age group associated with the reference risk curve to be aged 55–59 years. For cohort studies, the age range associated with the RR estimate was calculated as a mean age at baseline plus the mean/median years of follow-up (if only the maximum years of follow-up were reported, we would halve this value and add it to the mean age at baseline). For case–control studies, the age range associated with the OR estimate was simply the reported mean age at baseline (if mean age was not reported, we used the midpoint of the age range instead).
In the third step, we extracted age group-specific RR data and relevant bias covariates from the studies identified in our systematic review approach of age-specific smoking risk on CVD outcomes, and used MR-BRT to model the age pattern of excess risk (that is, RR-1) of smoking on CVD outcomes with age group-specific excess RR data for all CVD outcomes. We modeled the age pattern of smoking risk on CVDs following the same steps we implemented for modeling dose–response risk curves. In the final model, we included a spline on age, random slope on age by study and the bias covariate encoding exposure definition (that is, current, former and ever smokers), which was picked by the variable selection algorithm 28 , 29 . When predicting the age pattern of the excess risk of smoking on CVD outcomes using the fitted model, we did not include between-study heterogeneity to reduce uncertainty in the prediction.
In the fourth step, we calculated the age attenuation factors of excess risk compared with the reference age group for each CVD outcome as the ratio of the estimated excess risk for each age group to the excess risk for the reference age group. We performed the calculation at the draw level to obtain 1,000 draws of the age attenuation factors for each age group. Once we had estimated the age attenuation factors, we carried out the last step, which consisted of adjusting the risk curve for the reference age group from step 1 using equation (1) to produce the age group-specific risk curves for each CVD outcome:
We implemented the age adjustment at the draw level so that the uncertainty of the age attenuation factors could be naturally incorporated into the final adjusted age-specific RR curves. A PRISMA diagram detailing the systematic review approach, a description of the studies included and the full details about the methods are in Supplementary Information 1.5 and 5.2 .
Estimating the theoretical minimum risk exposure level
The theoretical minimum risk exposure level for smoking was 0, that is, no individuals in the population are current or former smokers.
Model validation
The validity of the meta-analytic tool has been extensively evaluated by Zheng and colleagues using simulation experiments 28 , 29 . For the present study, we conducted two additional sensitivity analyses to examine how the shape of the risk curves was impacted by applying a monotonicity constraint and trimming 10% of data. We present the results of these sensitivity analyses in Supplementary Information 6 . In addition to the sensitivity analyses, the dose–response risk estimates were also validated by plotting the mean risk function along with its 95% UI against both the extracted dose-specific RR data from the studies included and our previous dose–response risk estimates from the GBD 2019 (ref. 30 ). The mean risk functions along with the 95% UIs were validated based on data fit and the level, shape and plausibility of the dose–response risk curves. All curves were validated by all authors and reviewed by an external expert panel, comprising professors with relevant experience from universities including Johns Hopkins University, Karolinska Institute and University of Barcelona; senior scientists working in relevant departments at the WHO and the Center for Disease Control and Prevention (CDC) and directors of nongovernmental organizations such as the Campaign for Tobacco-Free Kids.
Statistical analysis
Analyses were carried out using R v.3.6.3, Python v.3.8 and Stata v.16.
Statistics and reproducibility
The study was a secondary analysis of existing data involving systematic reviews and meta-analyses. No statistical method was used to predetermine sample size. As the study did not involve primary data collection, randomization and blinding, data exclusions were not relevant to the present study, and, as such, no data were excluded and we performed no randomization or blinding. We have made our data and code available to foster reproducibility.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availability
The findings from the present study are supported by data available in the published literature. Data sources and citations for each risk–outcome pair can be downloaded using the ‘download’ button on each risk curve page currently available at https://vizhub.healthdata.org/burden-of-proof . Study characteristics and citations for all input data used in the analyses are also provided in Supplementary Table 3 , and Supplementary Table 2 provides a template of the data collection form.
Code availability
All code used for these analyses is publicly available online ( https://github.com/ihmeuw-msca/burden-of-proof ).
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Acknowledgements
Research reported in this publication was supported by the Bill & Melinda Gates Foundation and Bloomberg Philanthropies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. The study funders had no role in study design, data collection, data analysis, data interpretation, writing of the final report or the decision to publish.
We thank the Tobacco Metrics Team Advisory Group for their valuable input and review of the work. The members of the Advisory Group are: P. Allebeck, R. Chandora, J. Drope, M. Eriksen, E. Fernández, H. Gouda, R. Kennedy, D. McGoldrick, L. Pan, K. Schotte, E. Sebrie, J. Soriano, M. Tynan and K. Welding.
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X.D., S.I.H., S.A.M., E.C.M., E.M.O., C.J.L.M. and E.G. managed the estimation or publications process. X.D. and G.F.G. wrote the first draft of the manuscript. X.D. and P.Z. had primary responsibility for applying analytical methods to produce estimates. X.D., G.F.G., N.S.A., J.A.A., S.C., R.F., V.I., M.J.M., L.M., S.I.N., C.O., M.B.R. and J.W. had primary responsibility for seeking, cataloguing, extracting or cleaning data, and for designing or coding figures and tables. X.D., G.F.G., M.B.R., N.S.A., H.R.L., C.O. and J.W. provided data or critical feedback on data sources. X.D., J.H., R.J.D.S., A.Y.A., P.Z., C.J.L.M. and E.G. developed methods or computational machinery. X.D., G.F.G., M.B.R., S.I.H., J.H., R.J.D.S., A.Y.A., P.Z., C.J.L.M. and E.G. provided critical feedback on methods or results. X.D., G.F.G., M.B.R., C.B., S.I.H., L.B.M., S.A.M., A.Y.A. and E.G. drafted the work or revised it critically for important intellectual content. X.D., S.I.H., L.B.M., E.C.M., E.M.O. and E.G. managed the overall research enterprise.
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Extended data
Extended data fig. 1 prisma 2020 flow diagram for an updated systematic review of the smoking and tracheal, bronchus, and lung cancer risk-outcome pair..
The PRISMA flow diagram of an updated systematic review on the relationship between smoking and lung cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .
Extended Data Fig. 2 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Chronic obstructive pulmonary disease risk-outcome pair.
The PRISMA flow diagram of an updated systematic review on the relationship between smoking and chronic obstructive pulmonary disease conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .
Extended Data Fig. 3 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Diabetes mellitus type 2 risk- outcome pair.
The PRISMA flow diagram of an updated systematic review on the relationship between smoking and type 2 diabetes conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .
Extended Data Fig. 4 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Breast cancer risk-outcome pair.
The PRISMA flow diagram of an updated systematic review on the relationship between smoking and breast cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .
Extended Data Fig. 5 PRISMA 2020 flow diagram for an updated systematic review of the Smoking and Prostate cancer risk-outcome pair.
The PRISMA flow diagram of an updated systematic review on the relationship between smoking and prostate cancer conducted on PubMed to update historical review from previous cycles of the Global Burden of Disease Study. Template is from: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/ .
Extended Data Fig. 6 Smoking and Breast Cancer.
a , log-relative risk function. b , relative risk function. c , A modified funnel plot showing the residuals (relative to 0) on the x-axis and the estimated standard deviation (SD) that includes reported SD and between-study heterogeneity on the y-axis.
Supplementary information
Supplementary information.
Supplementary Information 1: Data source identification and assessment. Supplementary Information 2: Data inputs. Supplementary Information 3: Study quality and bias assessment. Supplementary Information 4: The dose–response RR curves and their 95% UIs for all smoking–outcome pairs. Supplementary Information 5: Supplementary methods. Supplementary Information 6: Sensitivity analysis. Supplementary Information 7: Binary smoking–outcome pair. Supplementary Information 8: Risk curve details. Supplementary Information 9: GATHER and PRISMA checklists.
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Dai, X., Gil, G.F., Reitsma, M.B. et al. Health effects associated with smoking: a Burden of Proof study. Nat Med 28 , 2045–2055 (2022). https://doi.org/10.1038/s41591-022-01978-x
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The Study That Helped Spur the U.S. Stop-Smoking Movement
Most Americans born into the generations that came after the Baby Boom have gone their entire lives aware that smoking can cause lung cancer. But this fact has not always been well-known – and at one time it wasn’t known at all.
Actually, it wasn’t even until cigarettes were mass produced and popularized by manufacturers in the first part of the 20th century that there was cause for alarm. Prior to the 1900s, lung cancer was a rare disease. Turn-of-the-century changes though, gave way to an era of rapidly increasing lung cancer rates. New technology allowed cigarettes to be produced on a large scale, and advertising glamorized smoking. The military got in on it too – giving cigarettes out for free to soldiers during World Wars I and II.
Cigarette smoking increased rapidly through the 1950s, becoming much more widespread. Per capita cigarette consumption soared from 54 per year in 1900, to 4,345 per year in 1963. And, lung cancer went from rarity to more commonplace – by the early 1950s it became “the most common cancer diagnosed in American men,” writes American Cancer Society Chief Medical Officer Otis Brawley, M.D., in an article published November 2013 in CA: A Cancer Journal for Clinicians .
However, though tobacco usage and lung cancer rates increased in tandem, few experts suspected a connection, according to Brawley and his co-authors.
There were a few small-scale studies conducted from the late 1920s to late 1940s that suggested a possible link between smoking and lung cancer, but these studies had several limitations – and didn’t provide the evidence necessary to establish a clear connection between smoking and lung cancer.
This began to change in the 1950s. Five larger retrospective studies were published in the early 1950’s that again showed a link between cigarette smoking and lung cancer. Though important, these studies still didn’t make a convincing enough case as they relied on the self-reported smoking habits of people who already had lung cancer, and compared them to those who didn’t. One potential problem with this type of study is that people with lung cancer are more likely to overestimate how much they smoked, while those who don’t have lung cancer are more likely to underestimate how much they smoked.
To address this issue, a prospective (cohort) study was needed – recruiting healthy people and following them over time to see who develops or dies from lung cancer and who does not. Without such evidence, the tobacco industry was able to cast doubt on the link between smoking and death from lung cancer and other diseases, says Eric Jacobs, Ph.D., an epidemiologist at the American Cancer Society.
Two American Cancer Society Researchers Get to Work
To address the criticism of the retrospective studies – and to strengthen the evidence that smoking is a cause of lung cancer – E. Cuyler Hammond, Ph.D., and Daniel Horn, Ph.D., scientists working for the American Cancer Society, started work on what is known as a cohort study.
In January 1952, Hammond and Horn engaged 22,000 American Cancer Society volunteers to help recruit a large group of American men aged 50 to 69 across 10 U.S. states and ask these men about their smoking habits. The scientists ended up with a cohort of about 188,000 men, who they eventually followed through 1955.
The participants were asked whether they smoked cigarettes, if they did smoke how often they smoked, and how many cigarettes they smoked. They were asked about both their current and past smoking habits. The questionnaire also asked about cigar and pipe smoking.
In November 1952, the volunteers began the first follow up. Each volunteer was in charge of 5 to 10 men. When the volunteer researchers followed up with their participants, they were required to check on the questionnaire whether the man was “alive,” “dead,” or “don’t know.” Hammond and Horn then obtained copies of the official death certificates of all the men who died to confirm their cause of death.
‘Cause and Effect Relationships’
After following the men for about 20 months, Hammond and Horn had enough information to publish what they called “preliminary” findings in an August 7, 1954 Journal of the American Medical Association article. Their conclusion was clear: “It was found that men with a history of regular cigarette smoking have a considerably higher death rate than men who have never smoked or men who have smoked only cigars or pipes,” the researchers wrote.
Hammond and Horn noted that the higher death rate in smokers was due primarily to heart disease and cancer. “Deaths from cancer were definitely associated with regular cigarette smoking.” They called out lung cancer in particular: “The death rate from lung cancer was much higher among men with a history of regular cigarette smoking than among men who never smoked regularly.”
These two researchers finally felt they had the convincing evidence that cigarette smoking was a cause of lung cancer that the world was previously lacking. They ended their 1954 paper stating “… we are of the opinion that the associations found between regular cigarette smoking and death rates from diseases of the coronary arteries and between regular cigarette smoking and death rates from lung cancer reflect cause and effect relationships.”
Hammond and Horn were so convinced by these findings that they had presented them a couple months earlier, in June of 1954, at the American Medical Association’s annual conference. Previously heavy cigarette smokers, Hammond and Horn changed to pipes by the time of the meeting (although they later concluded that pipe smoking was also cancer causing).
Hammond and Horn’s results were uniquely important at the time, says Susan Gapstur, Ph.D., vice president of the American Cancer Society’s epidemiology research program. “Their study – along with the British Doctor’s study conducted around the same time – were the first two large prospective studies to establish a link between smoking and the subsequent risk of death from lung cancer and other diseases.”
An Even Bigger Study and a Letter to President Kennedy
After his success with the first cohort study, Hammond and the American Cancer Society in 1959 started a larger and more robust long-term follow-up study, called Cancer Prevention Study I (CPS-I). This time, 68,000 volunteers, across 25 states, recruited more than 1 million men and women.
The data Hammond collected through this study provided further conclusive evidence about the harmful effects of smoking and were a major contributor to the landmark 1964 Surgeon General’s Report on Smoking and Health. That report led to sweeping tobacco policy changes in the United States and played a significant role in curbing smoking throughout the nation.
The creation of that landscape-altering report began with a letter sent to President John F. Kennedy in June 1961. In it, leaders from the American Cancer Society, the American Public Health Association, and the National Tuberculosis Association urged Kennedy to form a national commission on smoking to find “a solution to this health problem …” Kennedy asked his surgeon general, Luther Terry, to tackle this.
Terry formed an advisory committee to study the available evidence on smoking and health. Over the course of more than a year, the members analyzed 16 independent studies, conducted in 5 different countries, over a period of 18 years.
“The principal data on the death rates of smokers of various types and of nonsmokers come from 7 large prospective studies of men,” according to the 1964 surgeon general’s report. These studies, when combined, consisted of data from 1,123,000 men, more than half of whom came from the American Cancer Society’s Hammond-Horn Study and Cancer Prevention Study-I.
Terry published the final report January 11, 1964 – 50 years ago. It concluded that: “Cigarette smoking is a health hazard of sufficient importance in the United States to warrant appropriate remedial action.”
That strong judgment fueled stop-smoking efforts across the United States. And since that time, the U.S. smoking rate has dropped by more than half.
Though it took many years after smoking started to decline for the lung cancer death rate to begin to come down, over time, it did – dramatically so for men. In men, lung cancer death rates have declined about 34% from their peak in 1990. In women, lung cancer death rates did not begin to decrease until 2003 because women started smoking in large numbers about 2 decades later than men. The lung cancer death rate among women is now 9% less than it was at its peak in 2002 and is expected to continue declining.
Questions Yet to Answer About Smoking and Health
Although progress has been made , millions of Americans still smoke – and die from – cigarettes. To review the strides the U.S. has made over the past 50 years and provide a call to action for what is left to be done to address tobacco use, the surgeon general will publish a new report on smoking and health in late January.
The report draws on the research that the American Cancer Society and others have continued to do since the time of Hammond and Horn. “The importance of continuing to document the high number of deaths due to cigarettes cannot be overestimated,” says Gapstur, whose team continues to conduct large long-term follow-up studies in the U.S.
Additionally, not every question about the effects of smoking on health has been answered yet. Gapstur and Jacobs say that questions remain about issues such as: exposure to secondhand smoke, particularly in childhood; the effects of e-cigarettes on smoking initiation and cessation; and which former smokers are at high enough risk to benefit from lung cancer screening.
As researchers continue to study smoking and health, additional anti-tobacco efforts are still needed, according to Tom Glynn, Ph.D., director of international cancer control for the American Cancer Society. “Nearly half a million Americans and 6 million people worldwide will die from tobacco use in 2014 – but we know what to do to stop that,” Glynn says.
He calls for implementing the World Health Organization’s global tobacco treaty, continuing to raise taxes on tobacco products, making smoke-free environments the norm rather than the exception, and ensuring science-based tobacco dependence treatment is available to everyone who wants to stop using tobacco. Glynn also wants “to encourage every country to develop the political and financial will to eliminate tobacco as a source of health and economic disruption.”
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I was just 11 when I started smoking in the 1980s. Finding cigarettes was easy. My mom was a smoker, and when she reached the end of a pack, she’d send me with a dollar to the corner store to buy more. Back then, there were no laws against selling tobacco products to kids, so the store clerk thought nothing of it.
Sometimes, I’d sneak cigarettes from my mom’s supply. Other times, I’d get them from the “cool” kids at school who let me join their clandestine smoking sessions.
By the time I graduated from college, I was smoking a pack a day.
Children motivated me to quit smoking
I wanted to quit, but my job as a social worker for abused and neglected children often left me emotionally drained. Cigarettes helped ease the stress. I tried several times to quit cold turkey, but those attempts never lasted.
That changed when I got married and had children. During both of my pregnancies, I quit smoking. It wasn’t that difficult – it was as though my mind instructed my body to protect my babies. But as soon as my sons were born, I started smoking again to de-stress from the pressures of motherhood and my job.
Working with so many children in the foster care system sometimes made me sad and frustrated. They didn’t deserve to be in this position. As a social worker, I did what I could to help, but I wanted to do more.
Then I had an epiphany. I could apply to become a foster parent. One of the rules of fostering is that you’re not permitted to smoke around children. I quit again, and for the next eight years, I fostered a succession of children in my smoke-free home. I adopted two little girls – one with special needs – whose chances of finding a family were slim.
Weight gain and nicotine addiction
As my family grew, my marriage crumbled. I tried hard to balance everything, but it was a challenge. Since cigarettes were off-limits, I used food as a de-stressor. I’d always been heavy, even as a child. But now I weighed more than 350 pounds. I was exhausted, depressed and overwhelmed.
Then one magical day, I said “enough.” I was tired of feeling tired. I got on the treadmill. I started eating healthy foods. I lost over 200 pounds. And I filed for divorce. I’m a redhead, and my dad always said redheads are stubborn as mules. I lost the weight and got my life back on track out of sheer determination.
Losing so much weight left me with saggy, excess skin. My doctor said surgery was the only way to get rid of it, so I underwent a full-body lift. During the 17-hour operation, plastic surgeons worked on my neck, breasts, stomach, arms, legs … everything.
My recovery was very painful. One day, when I was hurting worse than usual, my mother handed me a cigarette and said, “Here, this will help.” I took one long puff, and BAM ! I was hooked again. Nicotine addiction is powerful.
I was disappointed to resume smoking, but in the back of my mind, I rationalized that smoking would help me keep off the weight I’d fought so hard to lose. I conveniently “forgot” that cigarettes could harm my body and take years off my life.
A new start
With my divorce finalized, I decided to leave my home state of Illinois for a fresh start in Texas. I packed up my children and headed to Houston, where I was hired as a social worker for difficult-to-place foster children with advanced needs.
I carry a case load of 30 kids, and I view them all as my kids. My goal is to place each one in a loving foster family or permanent home. When that happens, there’s no greater joy. But getting to that point can be very stressful.
Sometimes my days are intense. When I first arrived in Houston with no family or friends to turn to, cigarettes once again became my way to de-stress. I hated them, but I needed them.
Participating in a smoking cessation study
That changed one day when a radio advertisement caught my attention. “Are you tired of smoking?” the announcer asked. I yelled “YES,” though I was alone in the room. The ad was recruiting smokers for an MD Anderson tobacco-cessation clinical trial called PISCES, which stands for “Precision-Implemented Smoking Cessation Evaluation Study.”
The program was free of charge, the ad said. I didn’t believe it. I thought surely there must be a program fee, or I’d have to reimburse MD Anderson if I failed to stop smoking during the study. I decided to call anyway, and I’m glad I did.
The research coordinator explained that, unlike many other tobacco-cessation studies, PISCES is conducted remotely. I’d never have to go in person for a single visit. Everything would be done by phone and with video visits over the computer. That sounded great to me, especially since I live an hour away from MD Anderson .
During the study, I would be asked to collect my saliva and urine samples at home, and submit them to MD Anderson by courier. These, along with samples from other PISCES participants, would be analyzed for genetic markers which could potentially be used to tailor smoking cessation treatments for people based on their genetics.
I was eager to quit smoking and happy to contribute to the research, so I joined the study.
How I quit smoking
My plan was simple to follow. I took a daily oral medication named varenicline for 12 weeks. The medicine was mailed to my home. It was so convenient. Other participants received nicotine replacement therapy in the form of a patch, gum or lozenges. The study was randomized, meaning neither the researchers nor the participants chose who would receive which treatment.
Counselors and doctors regularly checked in with me by phone or over the computer. I got lots of support.
Anyone who failed to stop smoking after 12 weeks was switched to a different therapy or higher dose. That’s the beauty of MD Anderson – they don’t give up on you.
Today, I’m proud to call myself a nonsmoker. I haven’t touched a cigarette in over a year, and my cravings have disappeared.
I’ve learned that all those years of “self-medicating” with cigarettes to ease stress were actually causing more stress. The program taught me that while smoking makes you temporarily feel calmer by releasing a chemical into your brain, it wears off quickly and you feel worse than before you lit up. Today, I handle stress in healthier ways, like walking my dog, watching a movie or exercising.
I'm in a much better place now, in so many ways. If you want to quit, but you're afraid to try, contact MD Anderson. If it worked for me, it can work for you.
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As a former competitive CrossFit athlete, Katherine Norman sees herself as a healthy person. Over the last year, she also saw herself as a smoker.
“During the pandemic, I reverted to old coping skills, like smoking,” she says. “I had this cognitive dissonance because I thought of myself as a healthy person and, at the same, I was smoking up to a pack a day.”
She recently quit smoking with the help of tobacco cessation study at MD Anderson . Now, she’s looking forward to entering her 30s smoke-free and healthier than ever.
Smoking to cope with COVID-19 pandemic stress
When Houston restaurants closed due to the COVID-19 pandemic in March 2020, Katherine was unable to work and had to deal with sudden fear and uncertainty about when she’d be able to return to her job. The fact that she recently moved and her girlfriend worked as a travel nurse added to Katherine’s anxiety. With gyms closed and social distancing in full effect, her healthy outlets were limited, too.
“I’d wake up every day with anxiety in the pit of my stomach, thinking, ‘Am I going to have a job to go back to? How long is this going to last?’” Katherine says. By the time her restaurant re-opened in June 2020, she was smoking more than ever and struggling to quit.
LGBT tobacco disparities
Although Katherine first began smoking intermittently in high school, she never considered herself a regular smoker until the pandemic.
“My mother and grandmother were smokers, so cigarettes were definitely around the house growing up,” Katherine says. “I started smoking to deal with depression and anxiety from not knowing how to deal with being a lesbian in a Christian home.”
Studies have shown that the LGBTQ+ community is disproportionately affected by tobacco and that LGB female youth are more than three times as likely to smoke as straight females. The smoking cessation study Katherine joined at MD Anderson, called Project On-Track , was designed with tobacco disparities, including the LGBTQ+ community, in mind, to look at how different factors affect stress and smoking.
“I remember wanting to escape my feelings and not knowing how to manage them,” Katherine says. “Smoking was my way of regulating myself.” Eventually, Katherine found other outlets, like competitive CrossFit, that helped her smoke less.
Multiple quit attempts in the face of nicotine addiction
As businesses reopened, Katherine worked to return to healthier habits. “I’d go to the gym and meditate, so I looked like a person who takes care of herself, yet I was putting this poison in my body and trying to clear it out at the gym,” she says. “I have a lot of willpower, so I’d ask myself why I couldn’t just stop smoking. But it‘s an addiction.”
By the time she saw the advertisement for MD Anderson ’s smoking cessation studies, Katherine had already tried to quit several times on her own. For most people, it takes multiple quit attempts and setbacks before successfully abstaining from cigarettes.
Study provides tools to successfully quit smoking
Joining the study gave Katherine free access to nicotine patches (which she decided not use) and virtual behavioral counseling, as well as a stipend for participating. She supplemented the counseling sessions with books and podcasts from leading behavioral scientists to better understand her addiction.
“I started actually paying attention to how cigarettes made me feel, and I realized they’re expensive, they stink, and they make my lungs hurt,” Katherine says. “Cigarettes really just made me feel like crap.”
As she looked toward her 30 th birthday this summer, Katherine decided that quitting smoking wouldn’t be the only change she made for her new chapter of life. She started seeing a nutritionist, moved on from a breakup and dealt with other personal challenges.
“The past year was hard on all of us. It showed me the things I struggle with and how surprisingly deep they go,” Katherine says. “I decided to move forward on a lot of things, and being able to articulate that for myself was powerful. The difference with quitting smoking this time is that I was finally ready to be done.”
Learn more about participating in MD Anderson’s smoking and tobacco-related studies.
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- Smoking on the rise among young adults: implications for research and policy
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- Correspondence to: Paula M Lantz, PhD, Department of Health Management & Policy, University of Michigan School of Public Health, 109 Observatory Rd. Room M3116, Ann Arbor, MI 48109-2029, USA; plantz{at}umich.edu
This paper investigates hypotheses regarding the cause of the recent apparent increase in young adult smoking, compares trends in smoking among young adults with trends in the use of other substances, and considers the implications for youth tobacco control research and policy. Time series analyses of national data suggest that the recent observed increase in smoking among young adults is primarily an artefact of the almost simultaneous increase in smoking among high school students. In addition, however, it also appears that there have been real changes in smoking patterns among young adults. While many questions remain regarding recent trends in tobacco and other drug use among adolescents and young adults, what is known leads to a clarion call for increased intervention and policy action for the prevention and control of tobacco use among young adults in the USA.
- adolescence
- young adults, high school students
- college students
- smoking patterns
- CAS, College Alcohol Study
- CPS, Current Population Survey
- NHIS, National Health Interview Survey
http://dx.doi.org/10.1136/tc.12.suppl_1.i60
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In the late 1990s, evidence from a number of different sources pointed to a disquieting trend: cigarette smoking among college students in the USA was on the rise. Wechsler et al sounded one of the first alarms, reporting that longitudinal data from 130 college campuses showed that the prevalence of self reported smoking in the past 30 days increased from 22.3% in 1993 to 28.5% in 1997, an increase of approximately 28%. 1 A 1999 follow up survey (also conducted on a nationally representative sample of four year colleges) confirmed an increase in the prevalence of cigarette smoking among college students. 2
Data from the Monitoring the Future project also demonstrate that there was an increase in the prevalence of cigarette smoking among college students in the late 1990s. This upsurge, however, was observed among young adults in general—both those enrolled and not enrolled in college. 3 Between 1993 and 1999, the 30 day prevalence of cigarette smoking rose by 25% among college students 1–4 years beyond high school, and by about 21% for young adults 19–24 who were not in college. Although year 2000–01 data suggest a decrease in smoking among both college students and young adults not in school, these trends remain quite disturbing. At the present time, it is estimated that there are approximately 11 million smokers between the ages of 19 and 25 in the USA. 4
Several questions emerging from these general trends should resonate with those interested in youth tobacco prevention and control. A primary issue is the extent to which trends among young adults are reflections of previous trends in smoking behaviour among teenagers—that is, the apparent increase among young adults may be a cohort effect reflecting increased use among adolescents a few years earlier. In addition, however, these trends raise the spectre that there has been a true increase in the number of young adults initiating cigarette smoking after high school. From this, a cascade of concerns and additional questions flows. What might explain the apparent increase in the initiation of tobacco use among young adults? Is it possible that some tobacco control strategies aimed at adolescents are merely delaying or deferring initiation rather than preventing it? Have tobacco control advocates and policymakers been remiss in not focusing more resources on young adults?
The purpose of this paper is to explore the issue of recent trends in cigarette smoking among young adults in significant depth. This paper has the following objectives: (1) to review the published literature regarding trends in smoking behaviour among young adults in the USA over the past two decades, comparing college students and non-college young adults; (2) to investigate hypotheses regarding the causes of the apparent increase in smoking in the late 1990s; (3) to explore key issues that arise from a deeper understanding of these concurrent trends and various explanations regarding the causes and driving forces behind them; and (4) to make recommendations for tobacco control research and policy.
Review of published literature and trend data
Much of the information presented and reviewed in this paper was obtained from published manuscripts and abstracts. In addition, information on trends in adolescent and young adult smoking was obtained from a number of published reports and public websites. A major source of information for this paper came from the Monitoring the Future project, including a recent report on substance use among college students and adults aged 19–40 years. 3
Trend analysis of Monitoring the Future data
Annual data from the Monitoring the Future project were analysed using time series analytic techniques to detect whether or not the trend toward increasing smoking among high school students in the mid 1990s is related to the subsequent increase in smoking among college students. The goal here was to explore the hypothesis that the recent increase in young adult smoking is an artefact (or cohort effect) of the observed increase in smoking among teenagers. Using time series modelling (with lag functions), an analysis was conducted to see if there is statistical evidence of such a cohort effect. The dependent variable used for this analysis was 30 day prevalence of cigarette smoking (that is, whether or not someone had smoked a cigarette in the past 30 days).
Secondary analysis of 2000 NHIS data
Data from the 2000 National Health Interview Survey (NHIS) were analysed to assess recent trends in smoking behaviour among young adults. Following the methods of Pierce et al , 5 adult survey respondents were classified into birth cohorts (in this case, single year birth cohorts). Analyses were performed on 18 birth cohorts from 1960 through 1977. People in these birth cohorts turned 18 from 1978 to 1995, and turned 21 from 1981 to 1998. Restricting the sample to these birth cohorts means that all subjects for these analyses were 23–40 years old at the time of the NHIS 2000 survey. Including more recent birth cohorts was possible, but given that the people in these cohorts were 18–22 years old at the time of the survey and thus had not yet completed their early adult years, results regarding tobacco use patterns during this time period would be biased (and almost certainly in the direction of underestimation).
During the NHIS interview process, respondents who report smoking 100 or more cigarettes during their lifetime are categorised as “ever smokers” and asked a series of additional questions. The main variable of interest for this analysis was the age at which “ever smokers” reported becoming regular smokers (worded as “at what age did you become a fairly regular smoker?”). Subjects responded with an age, that they never were a regular smoker, that they didn’t know, or that they refused to answer the question. Age at initiation of regular smoking was recoded to create a number of new variables including the establishing of regular smoking before age 15, before age 18, at age 18, or at ages 19–21. Other NHIS variables under analysis included ones related to current cigarette smoking status, the use of other tobacco products, and smoking cessation behaviour.
Variables of interest were analysed by birth cohort to detect any trends or changes over time. Most analyses were conducted for the entire sample and by sex. All analyses used weighted data to correct for the complex sampling design of the NHIS. The strengths of the NHIS data—which present a cross-sectional picture across a number of age groups and thus birth cohorts—are that the experiences of several cohorts can be analysed using only one year of data. A limitation, however, is that everyone is answering questions in the year 2000, when they were asked to recall specific aspects of their smoking initiation and early smoking behaviour. The further people are away from these experiences, the more likely recall bias might influence their responses. Thus, any changes observed across cohorts may reflect, in part, the fact that older cohorts are further away from the actual experiences in question.
Recent trends in smoking among young adults
Monitoring the future results.
A very useful and informative source of information on trends in tobacco and other drug use among American youth is the Monitoring the Future project, an ongoing research programme conducted since 1975 out of the Institute for Social Research at the University of Michigan. 3, 6 Since 1976, a representative sample of the high school seniors participating in the Monitoring the Future surveys has received periodic follow up surveys. Thus, the Monitoring the Future project provides information on the drug related behaviour of high school students and adults through the age of 40 at the present time.
Data available from the Monitoring the Future Project demonstrate that there has been a significant increase in cigarette use among young adults during the past decade, for both those in and not in college. Figure 1 shows trends in the 30 day prevalence of cigarette smoking (panel A) and the 30 day prevalence of daily smoking (panel B) over the past two decades, for full time college students, for others 1–4 years past high school, and high school seniors. 3 For all three groups, the trends are similar. There was a decrease in cigarette smoking behaviour during the 1980s. In the early 1990s, however, smoking prevalence began to increase among all three groups, with an especially strong increase among high school seniors. The 30 day prevalence of smoking continued to increase through the 1990s for high school seniors, peaking in 1997 and decreasing thereafter. The trend lines for full time college students and other young adults peaked in 1999, and showed a decrease for the first time in many years in 2000.
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Monitoring the Future trend data regarding smoking. Reproduced from Johnston et al , 3 with permission. (A) Trends in 30 day prevalence of cigarette use. (B) Trends in 30 day prevalence of daily cigarette use.
Regarding college students, Johnston et al report: “Between 1990 and 1999, the 30-day prevalence of cigarette smoking by college students rose from 23% to 31%, or by about one-third, and daily smoking rose from 14% to 19%—or by about 40%.” 3 Between 1980 and 1994, female college students had higher rates of smoking than males. However, a crossover occurred in 1995, and since this time rates of smoking among college males have been slightly higher than those for females. It is believed that this crossover reflects a similar sex crossover in smoking behaviour that occurred among high school seniors a few years earlier. 3
Young adults 1–4 years beyond high school but not in college have a higher prevalence of smoking than those in college, with 30 day prevalence rates in the mid to late 1990s ranging from 35–42%. Rates of heavy smoking are also significantly greater among young adults not in college. For example, in the year 2000, 23.7% of young adults not in college reported smoking a half pack or more a day, compared with 10.1% of full time college students. Despite the greater prevalence of smoking among non-college young adults, the relative increases in smoking were much greater among college students. For example, between 1990 and 1999, the 30 day prevalence of daily smoking increased by approximately 25% for non-college young adults, yet by almost 60% for full time college students. 3
In summary, Monitoring the Future data provide clear and credible evidence that there were significant and alarming increases in cigarette smoking among both male and female high school students and young adults during the 1990s. 3, 6 Trends in self reported smoking behaviour are paralleled by trends in the percentage of high school seniors and young adults reporting that most or all of their friends smoke. 3 While the most recent data are suggestive of a decline or at least a plateau in the trend line, the results for the 1990s are unmistakable: high school students, college students, and young adults not in college all experienced a significant upsurge in cigarette smoking during the 1990s.
Harvard School of Public Health College Alcohol Studies
Another very useful source of information regarding trends in cigarette smoking among college students in the USA is the Harvard School of Public Health College Alcohol Study (CAS). The CAS involves a random sample of undergraduates at a nationally representative sample of four year colleges. While the primary focus of the survey is alcohol behaviour, information about cigarette smoking is included as well.
Results from the 1993 CAS included that 22.3% of full time college students had smoked in the past 30 days, with an additional 25% reporting that they were former smokers. 7 Wechsler et al looked at changes in smoking prevalence between the 1993 and 1997 surveys, and discovered the alarming finding reported above. 1 Over this five year period, there had been a 27.8% increase in cigarette smoking prevalence (defined as smoking during the past 30 days). In addition, it was reported that there was a decrease in smoking rates at the “extremes” of smoking behaviour: there were fewer very light smokers (< 1 cigarette per day) and fewer heavy smokers (> 20 per day).
More recently, Rigotti et al analyzed data from a third CAS, conducted in 1999. 2 Their findings included that more than 60% of college students had tried some sort of tobacco product in their life, that almost half (45.7%) had used some sort of tobacco product in the past year, and that a third (32.9%) were current users in terms of 30 day prevalence of any tobacco use. 2 The 30 day prevalence rate of cigarette use in 1999 was similar to the rate in 1997, suggesting a plateau in the upsurge among college students. While cigarettes accounted for the majority of tobacco ingested by college students in 1999, cigars also were a significant source of tobacco for males. Even though cigarette smoking rates were similar for males and females (28.4% v 28.5%, respectively), males had a higher overall rate of tobacco use in the past 30 days because of their higher use of cigars (15.7% v 3.9%) and smokeless tobacco (8.7% v 0.4%). 2
Other studies
Selected major findings from a number of data sources are summarised in table 1. The 1995 National College Health Risk Behaviour Survey included students at both two and four year institutions. 8 Nearly three quarters of the respondents (74.8%) reported that they had ever tried a cigarette, and 29% had smoked at least one cigarette in the past 30 days. Whites and those at two year institutions were more likely to report ever and recent smoking. This study was not longitudinal. Thus it cannot offer insights regarding trends.
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Prevalence of smoking in past 30 days among young adults in major national studies
Results from the NHIS suggest that the rate of current smoking among 18–24 year olds was 23.5% in 1991. 9 This rate had risen to 28.6% in 1997, a 22% increase. 10 Although the definition of current smoking in the NHIS changed slightly in 1992 (from having ever smoked 100 or more cigarettes and currently smoking to having ever smoked 100 or more cigarettes and now smoking every day or some days), this change is not responsible for the increase in smoking observed among 18–24 year olds during the 1990s. In fact, no other adult age group experienced an increase in smoking rates over this time; the only increase in current smoking occurred in the youngest adult age group.
Data from the NHIS also signal that, starting around 1997, the prevalence of smoking among those 18–24 years old was as high as those 25–44 years (for example, 28.7% v 28.6% in 1997, and 26.8% v 27.0% in 2000) (Gary Giovino, personal communication on unpublished NHIS results, May 2002). In prior years, NHIS results consistently showed the highest prevalence of smoking in the 25–44 age group, and significantly lower rates in the youngest adult years. This changed, however, in 1997 when two important trend lines met: while smoking rates for adults 25–44 years old were declining in the early to mid 1990s, they were simultaneously increasing among younger adults. NHIS data also suggest that, between 1997 and 2000, the prevalence of smoking among young adults 18–24 years old and those 25–44 was similar and was decreasing slightly.
NHIS results from the year 2000 (data not shown) also demonstrate that young adult males are significantly more likely to use other types of tobacco products than females, and that this pattern holds across birth cohorts. The use of cigars and bidis among males appears to have increased somewhat across birth cohorts reaching age 21 between the years of 1991 and 1998, and a significant proportion of males (over 25%) report having used smokeless tobacco products. This is alarming given the finding that smokeless tobacco use is a significant predictor of cigarette smoking initiation among young adult males. 11, 12 Also alarming are data suggesting that the consumption of cigars increased dramatically between 1993 and 1998, with a slight decrease after that year. 13
Major explanations for trends in young adult smoking
The compositional change hypothesis.
There are many possible explanations for the trends in young adult cigarette smoking described above. One explanation for the increase in smoking among college students is that it is an artefact of a compositional change in the US college student population. Historically, young adults not in college have had higher rates of smoking than those of a similar age but enrolled in college. 3 If more young adults are attending college, it is possible that increases in student smoking represent a change in the types of young adults attending school rather than a true increase in the prevalence of cigarette smoking. According to data from the Current Population Survey (CPS), 14 there has been a slight increase in the proportion of high school graduating seniors who attend college. In 1995, 62.0% of graduating high school seniors were enrolled in a college or university in the fall. This proportion climbed to 67.0% in the fall of 1997, which was a record high. In 1998, the proportion dropped to 65.6%, and fell to 62.9% in 1999 and 63.0% in 2000.
The CPS statistics suggest that the increase in the proportion of high school seniors attending college occurred at the same time that college smoking rates were increasing. Thus, it is possible that part of the increase in smoking observed among college students is due to a compositional change in the types of students who are attending college. The amount of the increase explained by such a change, however, is likely to be quite small. First, the increase in college enrolment, while certainly noteworthy, is not of such a magnitude that it would have a significant compositional effect. Second, there have been increases in smoking among young adults both in and not enrolled in college. Thus, the compositional change explanation, while worth considering, is not credible as a major explanation for the increase in young adult smoking.
The cohort effect hypothesis
A second hypothesis to consider is that the recent observed increase in smoking among young adults in the USA is an artefact of the almost simultaneous increase in smoking among high school students. Between 1991 and 1997, cigarette smoking among youth increased significantly according to several different data sources. 3, 15 Monitoring the Future data suggest a 32% increase in 30 day prevalence of any smoking among high school seniors during this time period. 10 Longitudinal analysis of Monitoring the Future data have established the existence of cohort effects: “if a class (or birth) cohort establishes an unusually high rate of smoking at an early age relative to other cohorts, the rate is likely to remain high throughout the life cycle relative to that of other birth cohorts at equivalent ages.” 3
Given the significant increase in smoking among high school students in the 1990s, the observed increase in smoking among young adults is often presumed to reflect the aging of adolescent cohorts with higher smoking rates. 2, 3 There are indeed data to support the cohort hypothesis. First, Monitoring the Future results do suggest that the increase in smoking among high school seniors predates the observed increase among young adults ages 19–24. An increase in 30 day prevalence of smoking was first observed in 1993 for high school seniors, and increases were subsequently observed for 19–20 year olds in 1994, for 21–22 year olds in 1995, and for 23–24 year olds in 1996. These results are quite suggestive of an aging cohort effect.
Second, time series modelling results suggest that the two trend lines—that is, trends in the 30 day smoking prevalence rates for high school seniors and young adults 1–4 years out of high school between 1980 and 2000—are significantly related to one another. Specifically, the results showed that rates of current smoking among high school seniors explained three quarters of the variance in the rates of smoking among all 19–20 year olds over the 21 year time period under study, using a one year lag function (R 2 = 0.756, p < 0.001). In addition, smoking rates among high school seniors explained two thirds of the variance in current smoking rates among all 21–22 year olds, using a three year lag function (R 2 = 0.67, p < 0.001). Similarly, smoking rates among high school seniors explained three quarters of the variance in smoking among college students 1–4 years out of high school, using a two year lag function (R 2 = 0.765, p < 0.001). Thus, rates of smoking among high school seniors are highly correlated with and explain the majority of the variance in subsequent smoking rates among young adults.
Nonetheless, there are also some disturbing aspects of observed trends that argue against a pure cohort effect. First, when the trend lines for college students and young adults not in school are separated (fig 1), one can see that increases in 30 day prevalence of any smoking and the 30 day prevalence of daily smoking among college students really started before 1990. 3 As such, it appears that the increase in smoking among college students actually was apparent before the upsurge in smoking among high school seniors (which appears to have started in 1992–93).
Second, similar to trends in cigarette consumption, the use of illicit drugs increased dramatically among high school students and young adults in the USA during the 1990s. 16, 17 Between 1980 and 1992, most illicit drugs showed a strong decrease in use among high school students, college students and young adults not in college. 3 After 1992, however, a number of drugs—including marijuana—showed a clear increase in use among adolescents (both males and females), with smaller increases for a number of substances among young adults. Rates of any illicit drug use increased in a startlingly way between 1992 and 2000 as follows: from 27.1% to 40.9% for high school seniors, from 29.7% to 39.3% for young adults ages 19–20, and from 30.0% to 36.9% for young adults ages 21–22. 3, 18 These rates began to stabilise slightly in the late 1990s.
Monitoring the Future investigators believe that much of the increase in illicit drug use among young adults is a cohort phenomenon, the result of adolescent cohorts with significant increases in use aging into young adulthood. 3 However, increases in the use of some drugs occurred simultaneously among high school students and young adults (both those in and not in college), including increases in the use of marijuana, hallucinogens, and amphetamines. Interestingly, Gledhill-Hoyt et al report that nearly a third of college marijuana users initiated use while in college. 16
Compared with other substances, trends in alcohol use are somewhat different. During the time period that smoking was significantly increasing among high school students and young adults in general, alcohol consumption was experiencing a slight increase in terms of 30 day prevalence and binge drinking. 19, 20 These increases are especially noteworthy as they started in the mid 1990s after a nearly decade long decline. 20, 21 Thus, while the observed increases are not as dramatic as those for smoking or for illicit drug use, these small increases represent a clear and definite shift in a trend line.
In summary, while the cohort hypothesis likely has some degree of explanatory power, it is probably not the full explanation for the observed increase in smoking among young adults. The story becomes more complex when we look at some of the details in the trend lines, and we also consider the fact that a broader phenomenon regarding other substance use among adolescents and young adults was occurring at the same time. In general it appears that increases in smoking have occurred at the same time as increases in the use of other tobacco products, binge drinking, and the use of many types of illicit drugs, including marijuana. Given the strong evidence that risk taking behaviour regarding substances in general was on the rise among youth and young adults during the 1990s, it is clear that the case of cigarette smoking should not be viewed as an isolated phenomenon.
The change in age at initiation or habitual smoking hypothesis
In attempting to explain the increase in cigarette smoking among young adults in the USA, it is also important to consider whether there have been any changes in the age of smoking initiation or habitual smoking. Becoming a regular or habitual smoker is described as a process or a series of transitions through several stages, starting with the first “initiating” puff on a cigarette. 22, 23
The epidemiology of cigarette smoking initiation in the USA includes one clear and consistent finding: the majority of people who end up being habitual smokers initiate experimentation with smoking as children or adolescents. 15 Data from a variety of sources consistently have shown that the vast majority of people who try a cigarette for the first time are under 18, and that the majority who become daily smokers do so by or at age 18.
There is no doubt that, even in the face of increased smoking rates among young adults, cigarette smoking initiation remains primarily an activity of minors. Even so, there may have been changes in some aspects of the process of being a regular or habitual smoker. In particular, it may be that there have been changes in age distribution of habitual or regular smoking. Below, results from an analysis of data from the 2000 NHIS regarding smoking behaviour in early adulthood across birth cohorts (from 1960 to 1977) are presented in an attempt to shed a bit more light on the hypothesis that there have been changes in age specific smoking patterns concomitant with the observed increase in cigarette use among young adults.
Trends in ever smoking 100 cigarettes
NHIS data from 2000 show that, across the 18 age cohorts, between a third and a half of adults ages 23–40 reported that they have smoked 100 or more cigarettes in their lifetime (table 2). There does appear to have been a slight increase in the proportion of adults reporting ever smoking 100 cigarettes for more recent birth cohorts (those turning 21 between 1996 and 1998), although this change is not statistically significant. There also appears to have been a slight increase in the rate of current smokers over time, with people from younger age cohorts (those born in 1975–77) having a higher rate of current smoking than those in older cohorts. This is consistent with the findings regarding an increase in smoking prevalence among young adults described above. However, given that the NHIS findings presented here involve cross-sectional analysis only, it is possible that this pattern is explained in part by older respondents having had more time to engage in smoking cessation.
2000 National Health Interview Survey results: smoking prevalence by birth cohort
Trends in age at initiation of regular smoking
The 2000 NHIS survey data suggest that the mean and median age at which regular smoking was established did not change much over these 18 birth cohorts. On average, over time, the mean age for regular smoking has been 20.8 years (with a median of 17 years). While there was a decrease in the mean age of establishment of regular smoking for the 1973 and 1974 birth cohorts (17.5 and 18.7 years, respectively), more recent birth cohorts have average ages of initiation of regular smoking that are quite similar to the general pattern over time.
As shown in table 3, the proportion of ever smokers establishing habitual smoking habits by the age of adulthood has fluctuated some over time, but does appear to have risen somewhat for cohorts born after 1970 (that is, for those turning 18 during the early 1990s). For example, 66.8% of ever smokers in the 1970 birth cohort became regular smokers by age 18, compared with 74.5% in the 1976 birth cohort. When coupled with trends regarding the mean age at initiation, these results support a well accepted tenet in tobacco prevention circles: that most smokers initiate smoking behaviour as adolescents, and that—for the vast majority of smokers—smoking became a regular activity or habit at age 18 or younger. Data from the 2000 NHIS do not suggest any sort of significant deviation from well established and well understood patterns of regarding youth smoking in the USA.
2000 National Health Interview Survey results: patterns in the age of initiation of regular smoking by birth cohort
However, the NHIS results do suggest that the rate at which ever smokers establish regular smoking between the ages of 19–21 has experienced an increase in recent years (table 3). In particular, the proportion of ever smokers who report the establishment of habitual smoking at the ages of 19, 20, or 21 appears to have increased for more recent birth cohorts, specifically the 1975–77 birth cohorts. People in these birth cohorts turned 21 between 1996 and 1998, years in which smoking among young adults was increasing. For example, 13.5% of ever smokers in the 1974 birth cohort reported becoming regular smokers in early adulthood (as opposed to age 18 or younger), compared with 17.8% in the 1975 birth cohort and 21.7% in the 1977 birth cohort (table 3). For the most recent cohorts, approximately one out of four smokers became a regular smoker between the ages of 19 and 21. Time series analysis shows that the change in the slope of this trend line is close to being significant at the 0.05 level (p = 0.058). While more data points are needed to reach any solid conclusions, the current data do suggest that a significant proportion of smokers are making the transition to habitual smoking as young adults and that this appears to have increased in frequency among more recent birth cohorts.
It is also interesting to note that the proportion of ever smokers (100 or more cigarettes) who report that they never became a “habitual smoker” was similarly low (less than 6%) across the birth cohorts under study (table 3). Thus, it appears that most people who have ever had 100 cigarettes become what they consider to be a regular smoker at some point, and that the rate of experimenters who do not become regular smokers has not changed much over the past two decades. Even so, some people who smoke regularly can do so without smoking daily. These people are referred to as “intermittent smokers”. 24 NHIS data show that the prevalence of intermittent or non-daily smokers has increased over the birth cohorts under study, from a rate of 12.1% of ever smokers in the 1960 cohort to 20.8% in the 1977 cohort (table 3). Time series analysis revealed that there has been a significant change in the slope of this trend line (p = 0.001), suggesting that the rate of intermittent smoking among adults is indeed higher among more recent birth cohorts, or those who reached young adulthood during the years where smoking prevalence among young adults was increasing.
Sex difference
Some interesting sex differences in smoking patterns are apparent in the 2000 NHIS data (table 4). First, the proportion reporting ever smoking 100 cigarettes is higher among males than females in all birth cohorts. Second, changes in the age at regular smoking are stronger for males than females. Using the last four years of available NHIS data (representing those who turned 23 between 1997 and 2000), the proportion of males establishing regular smoking by age 18 decreased from 80.3% for the 1974 birth cohort to 67.4% for the 1977 birth cohort, compared with 77.8% and 73.6% for the 1974 and 1977 birth cohorts, respectively, for females. Similarly, the proportion of males who reported becoming a regular smoker at ages 19–21 increased by 75% comparing the 1970 birth cohort with the 1977 birth cohort (14.0% v 24.5%). The proportion for females across this same time period increased by 5.5% (with some fluctuations—see table 4). These trend data suggest that the phenomenon of an increase in the rate of habitual smoking initiation is much stronger for males.
2000 National Health Interview Survey results: age at which respondent became a regular smoker by birth cohort and sex
In summary, the epidemiology of cigarette smoking indicates that smoking initiation primarily occurs during adolescence. Evidence from a number of sources suggests that this pattern has intensified during the past two decades. The majority of smokers are still trying their first cigarette in early adolescence, and making the transition to habitual smoking by age 19. However, it is also the case that a significant proportion of smokers establish regular or habitual smoking as young adults. Analyses of NHIS survey data suggest that this proportion has been sizeable for some time, and that it increased, particularly among males, during the late 1990s. In addition, the proportion of current smokers who do not smoke daily has significantly increased among younger birth cohorts.
These findings are paralleled by data from a number of other recent surveys, including results from the 1999 National Youth Tobacco Survey, which show that the proportion of 18 and 19 year olds classified as “non-daily smokers” or “experimenters” was greater than the proportion of current smokers. 25 In addition, trend data from the National Household Survey on Drug Abuse show that the rate of initiation of daily cigarette use among both 12–17 year olds and 18–25 year olds increased during the 1990s. 26 For young adults (18–25 years), the rate of initiation for daily smoking (per 1000 person years of exposure) jumped from 28.9 in 1990 to 34.7 in 1997. 26
Along with a “cohort effect” (whereby cohorts with increased rates of adolescent smoking carried their smoking rates into their young adult years), the increase in smoking prevalence among young adults also appears to be occurring because there has been an increase in the rate at which young adults who have experimented with cigarettes become regular smokers. For those who turned 21 in 1998, approximately 1 out of 5 female smokers and 1 out of 4 male smokers established regular smoking after the age of 18. It appears that there have been some changes in smoking behaviour patterns among young adults above and beyond an increase in prevalence caused by a cohort effect.
INDIVIDUAL RISK FACTORS AND SOCIAL ENVIRONMENT CONSIDERATIONS
Recent results from the 1998–99 Tobacco-Use Supplement to the CPS suggest that, among young adults ages 18–24, current smokers (26% of the sample overall) were more likely to be male (29%), white (31%) or American Indian (35%), unemployed (36%), or blue collar (34%) or services workers (32%). 27 There is very little in the published literature regarding risk factors for smoking among young adults not in college. In contrast, the Harvard College CAS have provided valuable insights regarding individual risk factors for smoking among college students.
Multivariable analysis of the 1993 CAS data led Emmons et al to conclude that other lifestyle choices are significantly associated with cigarette smoking in the past 30 days among college students. 7 This includes using marijuana, heavy drinking, and having multiple sex partners. The attitudes that parties are a very important or important part of college life and that collegiate athletics and religion are not very important also were significantly related to smoking. In addition, Emmons et al found that white students, those belonging to a fraternity or sorority, and women living in a co-ed dorm had a higher risk of smoking. Similar to findings from the 1993 CAS data, 1999 college students who used tobacco were more likely to be white and to experiment with other risky behaviours (for example, binge drinking, marijuana use, and multiple sexual partners) than non-smokers. 2
Analysing predictors of “late onset smoking” (defined as establishment of smoking after high school), Ellickson et al found that lower parental education, worse grades in high school, and younger age relative to others in a grade cohort were significant risk factors. 28 In a longitudinal study of college bound high school students who reported never experimenting with tobacco, Choi et al found that—four years later—14% had initiated smoking. 29 Risk factors for this late initiation included being white, having more depressive symptoms, attending church less often, believing that peers approve of smoking, and believing that experimenting with cigarettes is safe.
Wee et al found that adults younger than 30—both male and female—are more likely to smoke if they are trying to lose weight. 30 Weschler et al reported that a prominent perception among health centre directors on college campuses is that students smoke for a variety of reasons—including as a response to stress and as tool for weight control—and that many students do not believe they are addicted and that they will quit upon graduation. 31
In addition, it is believed that smoking reduces and, for some people, fully relieves anxiety in a variety of social situations. Sonntag et al reported that social anxiety has been significantly associated with nicotine dependence in both cross-sectional and longitudinal studies. 32 In addition Anda et al claimed that their results from the Adverse Childhood Experiences Study contribute to a growing literature suggesting that “nicotine use is associated with self-medicating efforts to cope with negative emotional and social experiences”. 33 A significant, graded relation was found between smoking and the number of adverse childhood experiences, including emotional, physical, and sexual abuse; a battered mother; parental separation/divorce; and growing up with a substance abusing, mentally ill, or incarcerated household member.
Are adolescents and young adults smoking cigarettes more because of increased feelings of social anxiety and pressure? Are they trying to “self medicate” to relieve stress or emotional pain in some way? The myriad reasons that adolescents and young adults are smoking cigarettes and using other substances are complex and not well understood. A full review of the literature on this topic is outside of the scope of this paper. However, a prominent social environment hypothesis regarding the increase in smoking among young adults is that the tobacco industry has intensified its activity in this market segment—that is, more aggressive industry marketing activities may be partly responsible for the increase in smoking observed among college students and young adults in general.
Using tobacco industry documents that have become public in the wake of litigation, Katz and Lavack, 34 Sepe et al , 35 and Sepe and Glantz 36 have argued that changes in industry promotional tactics correspond with the increase in smoking observed among young adults. These marketing tactics have taken many forms since the late 1980s: (1) promotions in bars, nightclubs, comedy clubs, and other venues that use person-to-person interactions, free samples, free promotional accessories, contests, and games; (2) efforts to cultivate “brand presence” in bars, including company branded items (such as napkins, coasters, clothing for employees, etc), and financial incentives for owners and employees; and (3) increased use of the alternative press (especially weekly alternative newspapers in urban areas) for several purposes, including product advertisement, event promotion, and bar promotion.
Adult only facilities—such as bars and nightclubs—are exempt from the 1998 Master Settlement Agreement in terms of marketing activities. Sepe et al argue that tobacco industry bar and nightclub promotions “protect the industry from advertising regulations, clean indoor air laws, and accusations of marketing to adolescents. Bar promotions help the industry engineer peer influence to encourage tobacco use among young adults.” 35
Sepe and Glantz wrote that young adults “are not immune to ‘late’ initiation of smoking . . .. Directed marketing toward young adults in social settings such as bars and nightclubs may raise the age at initiation toward what it was in the past. Current increases in young adult smoking, in terms of both overall prevalence and first use, suggest that this directed marketing is having an impact.” 36 Thus, the argument is being made that observed increases in smoking among young adults are in part explained by tobacco industry promotional tactics.
There is a growing body of research literature reporting associations between exposure to tobacco industry marketing/promotions and smoking behaviours, particularly among youth. 37– 40 Thus, is not unreasonable to consider the hypothesis that increased efforts targeting young adults have reaped benefits for the industry. Although spending on tobacco advertising remained relatively constant between 1988 and 1998, promotional allowances tripled in size during this time period. 41, 42 As has been argued in the past, the industry’s continued investment in specific types of promotion and marketing suggests that those within in the industry itself must have some evidence or reason to believe that these tactics are effective. 43
Ling and Glantz have attempted to shed light on why the tobacco industry has intensified its marketing efforts among young adults. They explain:
“First, the industry views the transition from smoking the first cigarette to becoming a confirmed pack-a-day smoker as a series of stages that may extend to age 25, and it has developed marketing strategies not only to encourage initial experimentation (often as teens), but also to carry new smokers through each stage of this process. Second, industry marketers encourage solidification of smoking habits and increases in cigarette consumption by focusing on key transition moments when young adults adopt new behaviors, such as entering new workplaces, school, military, and especially leisure and social activities. Third, tobacco companies study young adults’ attitudes, social groups, values, aspirations, role models, and activities, and infiltrate both their physical and social environments.” 44
Evidence from industry documents confirms that the tobacco industry has invested significant time and resources into studying youth and young adult development, motivations, and social environments, and that this research has helped them to divide potential and actual smokers into different markets or segments. 22, 40, 45 As described above, a number of recent articles provide provocative new evidence and ideas regarding tobacco industry strategies and trends in smoking among young adults. A note of caution, however, needs to be raised. The evidence to date is of a simple ecological nature: smoking rates among young adults rose several years after the industry first introduced promotional activities in bars, nightclubs, and other venues targeting young adults (in the late 1980s), and shortly after these types of efforts were intensified (in the early to mid 1990s). Rigotti et al recently found that, controlling for a number of potential confounders, those college students who report exposure to bar and campus tobacco promotional events do have higher rates of smoking, and that this association is only observed among those who became smokers as adults. 46 However, showing temporal associations and establishing causation are, of course, two different things. Thus, although certainly provocative and compelling, the evidence to date does not conclusively show a causal link between industry tactics and the increase in smoking among young adults.
RESEARCH AND POLICY IMPLICATIONS
As described above, much has been written about the apparent increase in smoking among college students. Increases in smoking, however, have not been observed exclusively in the college population. Significant increases also have been witnessed among young adults in general, and importantly among high school students. There is credible evidence that some of the observed increase among young adults is an artifactual result of the aging of cohorts with increased smoking among youth. In addition, there is information indicating that other factors may be at play as well. Recent NHIS data suggest that there was an increase in the rate at which young adults became “regular” or habitual smokers at the same time as the observed increase in smoking prevalence, especially among males. In addition, the increase in cigarette smoking has occurred concomitantly with an increase in other risk taking behaviours regarding substance use, including binge drinking and the use of marijuana and other illicit drugs.
The reasons for the increase in smoking among young adults are not clear, and there are many questions that remain unanswered at this point in time. Additional research is needed in multiple areas, including research that will help to answer the following questions:
What sociodemographic and behavioural characteristics are associated with changes in the smoking behaviour of young adults? What subgroups are at higher risk for becoming habitual smokers as young adults? Are the characteristics or risk factors for habitual smoking initiation the same among college students and those not in school? Is the recent increase really largely a male phenomenon?
How is the increase in cigarette smoking among young adults related to the increase in the use of other substances? Are some of the same causal factors involved across substances?
Are there any tobacco control policies and interventions aimed at adolescents that may be delaying or deferring initiation of habitual smoking rather than preventing it?
How do we best intervene with adults who have just “come of age”? Are young adults more like adults or adolescents in terms of their knowledge and understanding of risk, their motivations, their self perceptions, their attitudes, the social influences that affect them, etc? What needs to be understood about young adults to better inform the design of smoking prevention and control interventions?
Even in the face of these and a number of other unanswered questions, we do have sufficient information and knowledge in hand to consider a number of programmatic and policy responses. The following is a list of potential policy responses and intervention strategies that need to be investigated, debated, and discussed as the tobacco control community further develops an agenda for addressing tobacco use among young adults.
Invest in smoking cessation interventions aimed at young adults
Although rates of smoking cessation have increased among adults over the past two decades, this has primarily been observed among adults ages 45 and older. Among young adults (ages 18–24), the percentage of ever smokers who have quit smoking has remained relatively stable, especially over the past 10 years (Gary Giovino, personal communication on unpublished NHIS results, May 2002). This does not mean, however, that young adult smokers are not interested in quitting. Results from the 1995 National College Health Risk Behaviour Survey included that 59% of current smokers had made at least one quit attempt, and that this rate was 82% among daily smokers. 47 Furthermore, recent results from the 2000 NHIS suggest that among those ages 18–24, over three quarters of current smokers who attempted to quit in the past still would like to quit, and that almost half (44.2%) of those who have zero quit attempts also would like to quit (Gary Giovino, personal communication on unpublished NHIS results, May 2002).
Given the prevalence of smoking and of the desire to quit among young adults, it is important that interventions and resources regarding smoking cessation be made available. According to the 2000 CPS, over 60% of young adults who graduated from high school are enrolled in a college or university; and 80% of young adults who are not full time students are in the labour force. Thus, a significant proportion of young adults can be reached with messages and resources offered through educational institutions and work sites.
Unfortunately, there is very little evaluation literature on smoking cessation interventions aimed at young adults. Thus, it is not possible at this time to make specific recommendations regarding cessation intervention approaches that have proven effective among young adults. We also should recognise that smoking cessation interventions that have been developed for adults in general may not be the best approach to take with younger adults. Those between the ages of 18–25 may be more like adolescents than older adults in their perceptions of risk, their perceptions of themselves as “smokers” or as having an addiction, their attitudes towards different types of cessation messages, and thus their responses to behavioural interventions. Thus, simply increasing the exposure of young adults to the existing arsenal of cessation tools/interventions is likely not the best way to proceed. A significant amount of formative research needs to be conducted in this area (for a start see O’Neill et al , 48 and Martinelli 49 ).
Even so, at this point in time it does seem reasonable to recommend that smoking cessation interventions that have been shown to be effective with adults in general be offered through student health services on college campuses, and that they be part of employee health benefit packages and resources, including typical employment venues of young adults not enrolled in college. Interventions should be tailored to address the attitudes and tobacco use patterns of young adults, recognising that a significant proportion have only recently become regular smokers or still may be intermittent smokers, and that a non-trivial proportion of males also use tobacco products other than cigarettes. Analysis of the impact of standard smoking cessation interventions in the young adult population need to be conducted so we can have some notion of their degree of effectiveness relative to older adults.
Unfortunately, many young adults are without health insurance, and people in this age group (especially males) do not have frequent contact with health care providers. Thus, it will admittedly be a challenge to expose young adult habitual smokers to proven cessation strategies involving clinical interventions combined with nicotine replacement therapy. In addition, data suggest that adolescents and young adults are infrequently asked about their smoking status and counselled regarding cessation during encounters with primary care providers. 50, 51 As such, interventions that do not rely on “teachable moments” with health care providers also must be designed and evaluated.
Wechsler et al conducted a survey of 393 college health centre directors to assess their attitudes about and efforts regarding student smoking. 52 The findings included that while 85% of directors considered smoking to be a serious problem, only 27% prohibit smoking in all indoor areas (which includes private offices and dormitories). In addition, almost half reported that there were no smoking cessation programmes available on their campus, and—among those who do provide cessation resources—the prominent perception was that demand was quite low. Similarly, a study conducted at 11 public colleges in Massachusetts found that “tobacco use among college students was not regarded as a high-priority problem by students or administrators”. 53 Thus, an obvious first step is to engage in efforts that will assist in making tobacco control a priority issue among college and university administrators and health care providers. It is likely that similar educational and “problem definition” efforts will have to be directed at employers and work site health managers.
Invest in smoking prevention interventions aimed at young adults
Given the epidemiology of smoking initiation, focusing prevention and control activities on youth has made great sense. 22, 37 However, the view that this focus on youth may be myopic and even dangerous in some ways has been expressed. Glantz has argued that a primary focus on youth in tobacco control efforts may be counterproductive, as it reinforces tobacco industry depictions of smoking as an “adult” behaviour, and shifts attention away from more comprehensive efforts. 54 Hill has made a similar argument, with a primary concern being that messages that youth should not smoke are likely to reinforce adolescents’ natural rebellious attitudes toward adults. 55 Even if one believes that it is essential to target serious tobacco prevention efforts toward youth, an important admission is that efforts to date, involving a wide variety of interventions, programmes and policies, have been met with limited success. 22, 37
Given current trends and the recognition that an increasing proportion of adult smokers initiate regular smoking after age 18, the time has come to increase prevention and control activities in the young adult population. It is still the case that the majority of smokers are fully engrained in this activity by the time they are 19. However, it appears that currently over 20% of smokers make the transition from occasional to habitual smoker as young adults. As Ling and Glantz have argued: “During the critical years of young adulthood, public health efforts dwindle while tobacco industry efforts intensify . . . Public health efforts should match tobacco industry interest in young adults. Each place where young adults adopt new behaviours also provides opportunities for public health interventions.” 44
Just what these prevention interventions and policies should look like is unclear at the moment. Again, there is very little literature regarding efforts to prevent tobacco use among young adults. Ling and Glantz suggest: “public health campaigns that resonate with the psychological needs and values of both smokers and nonsmokers may improve smoking prevention and cessation efforts. Interventions that affect cigarette prices, acceptance of the tobacco industry, the social acceptability of smoking, and secondhand tobacco smoke particularly threaten the industry.” 45 Jacobson et al suggested that public health practitioners and policymakers can learn a great deal from how the tobacco industry has skilfully marketed its products: “Just as tobacco marketing can influence smoking behaviour, social marketing is a promising approach to smoking prevention, although it does require significant resources and skillful execution.” The literature on social marketing suggests that mass media campaigns increase their chance for effectiveness if: (1) the campaign strategies are based on sound social marketing principles; (2) the effort is large and intense; (3) target groups are carefully differentiated; (4) messages for specific target groups are based on empirical findings regarding the attitudes, beliefs, needs and interests of the groups; and (5) the campaign is of sufficient duration. 22 Ling and Glantz recommend that media messages should not simply attempt to convince individuals not to smoke. 44 They also should support clean indoor air policies, social environments that challenge the social acceptability of smoking, and tobacco excise taxes.
Focusing prevention and control activities among young adults begs the question of the relative degree of focus on the college versus non-college populations. Given that smoking rates have increased more among college students than those not in school, and given the attention that the tobacco industry is giving to this market, one could argue that college students should be the number one priority. However, it is also the case that smoking rates are significantly higher among those not in school. It is likely that the most effective interventions will need to be tailored differently for these different groups of young adults. Tough discussions regarding the best use of limited prevention resources need to occur.
Consider potential counterproductive effects of interventions targeting adolescents
Even if one believes that a strong focus on youth is essential, it is possible that specific types of interventions and strategies are having the counterproductive effect of delaying rather than totally preventing tobacco use. Thus, it is important to consider whether any current youth focused strategies are delaying, deferring, or even encouraging smoking initiation among young adults rather than preventing it. To answer this issue, we would need to review a wide range of evaluations in which the long term effects of interventions were actually tracked into the early adult years. Unfortunately, follow up periods for youth tobacco interventions rarely extend beyond adolescence. An exception to this is the work of Rigotti et al , who analysed 1999 data regarding tobacco use among students at public colleges in Massachusetts and found that those students from this state (and thus ostensibly exposed to the Massachusetts’s youth focused tobacco control programme) had significantly lower rates of current use than those who attended high school in another state (31.5% v 42.6%). 56 These results suggest that exposure to a multiple component, comprehensive tobacco control programme as an adolescent has positive effects that last into young adulthood.
Additional information on the long term effects of youth tobacco prevention and control activities is greatly needed. In addition, it is critical that the tobacco control community invest some time and energy into considering whether or not specific types of youth focused strategies do indeed have the potential for counterproductive delaying effects. For example, it has become increasingly common for youth in possession of cigarettes to receive sanctions through the legal system (including such penalties as a ticket/fine or loss of driving privileges). 22, 54, 57 Such sanctions, of course, do not apply to adults. These negative consequences likely do not prevent experimentation with smoking, yet they may actually reduce youth access and/or persuade some minors to avoid smoking in public places. If such sanctions actually do decrease some minors’ ability and/or willingness to smoke, the transition to habitual smoking may be delayed. However, the desire to be “rebellious” and to engage in what is sanctioned as adult behaviour may have been reinforced (perhaps even intensified) and may remain strong as the adolescent reaches the “legal age” for the behaviour. As such, this may lead to increased smoking among young adults. While one might consider the proposed scenario rather far-fetched, it does seem worthwhile to contemplate potential negative side effects of various types of youth focused interventions. We need to entertain the uncomfortable possibility that strategies and tactics focusing on youth tobacco control are in part contributing to the recent changes in smoking behaviour observed among young adults.
Promote smoke-free environments
The promotion of smoke-free environments should be considered a potentially effective mechanism for decreasing smoking among young adults. These environments include work sites, campuses, restaurants, bars and nightclubs, and even homes. There is a growing amount of evidence that clean indoor air policies can have a positive effect on smokers as well as those at risk for exposure to environmental tobacco smoke. 22, 58 Such policies create social environments that reinforce messages about the negative aspects of tobacco smoke. Such environments may also encourage current smokers to quit or reduce their consumption, and in doing so may prevent some smokers from transitioning into regular, habitual smoking.
Ling and Glantz recommend the promotion of smoke-free homes among young adults: “ . . .educating young adults about the dangers of secondhand smoke may be particularly effective because they are starting new households and new families. Educating young adult parents (and parents to be) about the dangers of secondhand smoke will provide benefits not only for the new child (who will avoid the morbidity associated with involuntary smoking) but may also prompt cessation among the adults.” 44
Several people have argued for the creation of campus wide smoke-free environments (including dormitories and other residences, eating and recreation facilities, classrooms, and private offices). 5, 52 The results of their survey of college health centre directors suggest that some schools are attempting to counter trends in student smoking by implementing no-smoking policies. Such policies, however, must be promoted and enforced if they are to have any effect. Regarding smoke-free bars, Sepe et al stated that the “[c]reation of smoke-free bars—with appropriate ground-work and public education—may be a key to undermining the tobacco industry’s efforts to use bars to reestablish the social acceptability of smoking and secondhand smoke”. 35
Consider smoking in a broader context of risk taking behaviour
Adolescents and young adults will be done a great disservice if researchers and policy advocates do not consider tobacco use in the larger context of social environments and risk taking behaviour, in particular risky sexual behaviour and the use of alcohol and illicit drugs. A tobacco focused approach to policy and intervention is not likely to be the most effective strategy, since it is clear that a number of risky behaviours are linked with each other and with some identifiable attitudes and perspectives. The root causes of youth and young adult smoking are likely not tobacco specific, but rather things that motivate or drive people to engage in other forms of risky or rebellious behaviour. Malcolm Gladwell, in his book The tipping point , makes the important observation that what leads to smoking is not positive perceptions or attitudes about the act of smoking itself: “Over the past decade, the anti-smoking movement has railed against the tobacco companies for making smoking cool and has spent untold millions of dollars of public money trying to convince teenagers that smoking isn’t cool But that’s not the point. Smoking was never cool. Smokers are cool..” 59 Thus, it is possible that risk taking behaviours cluster together because adolescents (or young adults) are trying to project an overall image or persona of themselves that they view positively (a person who is rebellious, takes risk, is independent, etc). As such, the phenomenon may be more about a process of attempting to become a specific type of person than an isolated decision to engage in a specific type of behaviour (that is, cigarette smoking). Furthermore, while it is certainly reasonable to point fingers at the tobacco industry, insinuating sole blame on industry marketing tactics for the broad phenomenon of increased smoking among young adults is too simplistic. Efforts to reduce tobacco use among young adults need to include, but also have a broader vision and scope than, counteracting industry marketing/promotional activities
The recent increase in smoking among young adults should be of grave concern to those engaged in tobacco prevention and control among youth. The increase is partly a residual effect of increases in cigarette smoking that have occurred among adolescents. In addition, however, the upsurge in smoking among young adults appears to be part of a broader phenomenon involving changes in substance use and risk taking behaviours among youth making the transition to adulthood. While there are many unanswered questions about recent trends in cigarette smoking and other drug use among both adolescents and young adults, what is known to date leads to a clarion call for increased intervention and policy action regarding the prevention and control of substance abuse among young adults—both on and off campus—in the USA.
Acknowledgments
Nicole Kuiper provided valuable research assistance and support on this project. David Mendez and Harold Pollack provided guidance regarding analysis, and Gary Giovino offered useful data and comments. Nancy Rigotti, Pam Ling, Ken Warner, Peter Jacobson, and Ernest Dopp provided excellent substantive comments on earlier drafts of the paper. In addition, numerous colleagues attending the Innovations in Youth Tobacco Control Conference (July 2002) shared useful and provocative feedback.
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Welcome to Penn State Center for Research on Tobacco and Health
Penn State Center for Research on Tobacco and Health’s mission is to be a national leader of scientific discovery that will translate into effective interventions and policies to reduce tobacco-caused harm in our communities.
The center comprises a multidisciplinary team of experts, including basic scientists, public health researchers and medical professionals, all dedicated to improving the lives of people touched by tobacco and nicotine addiction. It is based at Penn State College of Medicine and Penn State Health Milton S. Hershey Medical Center in Hershey, Pa.
More than 16 million Americans are living with a disease caused by smoking. Across the world, tobacco is the leading cause of preventable death. The center’s overall goal is to conduct innovative research that will inform future policymaking on tobacco and health, investigate the toxicology and addictiveness of new and existing tobacco products and discover new treatments for addiction.
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- Center history
The overarching goal of TCORS program was to generate scientific evidence that would inform the regulation of tobacco products in order to reduce the public health burden from tobacco use. The TCORS program included three separate projects:
- Switching to progressively reduced nicotine content cigarettes in smokers with lower socioeconomic status (partnered with Kimberly Horn at George Washington University)
- Reduced nicotine cigarettes in smokers with mood and anxiety disorders (partnered with Eden Evins at Harvard University)
- Switching to reduced oxidant or nicotine content cigarettes in smokers
In addition, the projects were supported by multiple cores – and administrative core; a biomarker and analytic chemistry core (director: Dr. John Richie); and a biostatistics and data management core.
Additionally, the TCORS program provided pilot funding to several investigators including Ping Du, Raghu Sinha, Yuan-Wan Sun, Ryan Elias, Kurt Kistler, Guodong Liu, Kun-Ming Chen, Dhimant Desai, Samantha Reilly, Reema Goel, Joshua Lambert, Robin Taylor-Wilson, Jeffrey Pu and Nengliang Yao.
Projects funded ranged from studies evaluating the impact of electronic cigarette use among HIV positive smokers to evaluating free radical production from charcoal filters.
The TCORS grant also had a training component that facilitated the education of young scientists in tobacco regulatory science research. Scholars in the program completed a series of courses on tobacco regulation, the epidemiology of smoking and biomarkers, and participated in networking events with scholars from other TCORS institutions. The program also supported several postdoctoral scholars.
Penn State Center for Research on Tobacco and Health is designed to expand on the accomplishments of the TCORS program.
Nicotine Dependence Index
The Penn State Nicotine Dependence Index was developed by Dr. Jonathan Foulds in 2011.
This 10-item scale (with scores ranging from 0 to 20) was developed to measure nicotine dependence across all nicotine product types, and an adapted version was the first dependence measure designed to evaluate electronic cigarette dependence.
The index is available for use by clinicians and researchers at other institutions using the proper citation.
MRI Electronic Aerosol Delivery System (MEADS)
The MRI Electronic Aerosol Delivery System was developed through a collaboration between the Center for Research on Tobacco and Health, the Center for NMR Research and University Park.
The device works in coordination with an olfactometer to deliver up to four e-cigarette aerosols for functional MRI experiments.
With questions on this device, contact Dr. Andrea Hobkirk or Dr. Zachary Bitzer.
See protocol paper in Substance Abuse: Research and Treatment
Laboratory space for the Center for Research on Tobacco and Health is available on the third floor of Penn State Cancer Institute.
The MRI laboratory is part of the Center for NMR Research at Penn State College of Medicine.
Penn State is also home to this human smoking and addiction laboratory space.
Penn State Center for Research on Tobacco and Health also makes use of this smoking health and behavior laboratory at Penn State University Park.
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Latest news on tobacco, nicotine, vaping and smoking
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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Cancer, cardiovascular diseases and chronic obstructive pulmonary disease continue to be the main health problems associated with cigarette smoking. An
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We found that, even after factoring in the heterogeneity between studies and other sources of uncertainty, smoking has a strong-to-very-strong
These two researchers finally felt they had the convincing evidence that cigarette smoking was a cause of lung cancer that the world was
If you are 18-60 years old and ready to quit smoking, we need you for a research study on nicotine dependence at the National Institute on Drug Abuse in
MD Anderson's prevention research includes smoking & tobacco-related studies that community members can participate in. Learn more about these studies here.
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The Center for Research on Tobacco and Health is currently conducting several studies on nicotine, smoking and health. Some of the studies are for people who
Cigarette smoking and exposure to tobacco smoke cause about 480,000 premature deaths each year in the United States (1). Of those premature