essay about smoking among students

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Tobacco Use Among Children and Teens

Cigarette smoking during childhood and adolescence causes significant health problems among young people, including an increase in the number and severity of respiratory illnesses, decreased physical fitness and potential effects on lung growth and function.  1

Most importantly, this is when an addiction to smoking takes hold, often lasting into and sometimes throughout adulthood. Among adults who have ever smoked daily, 87%  had tried their first cigarette by the time they were 18 years of age, and 95% had by age 21.  2  

Key Facts about Tobacco Use among Children and Teenagers

• Every day, almost 2,500 children under 18 years of age try their first cigarette, and more than 400 of them will become new, regular daily smokers.  3 Half of them will ultimately die from their habit.  4
• People who start smoking at an early age are more likely to develop a severe addiction to nicotine than those who start at a later age. Of adolescents who have smoked at least 100 cigarettes in their lifetime, most of them report that they would like to quit, but are not able to do so.  5
• If current tobacco use patterns persist, an estimated 5.6 million of today’s youth under age 18 eventually will die prematurely from a smoking-related disease.  6

Prevalence of Tobacco Use among Children and Teenagers

• In 2015, 9.3% of high school students reported smoking cigarettes in the last 30 days, down 74% from 36.4% in 1997 when rates peaked after increasing throughout the first half of the 1990s.  7
• In 2015, 2.3% of middle school students smoked cigarettes in the last 30 days. 7
• Rates of overall tobacco use remain high, however. In 2015, 25.3% of high school students and 7.4% of middle school students used a tobacco product.  7
• Among high school students in 2015, the most prevalent forms of tobacco used were electronic cigarettes (16%), cigarettes (9.3%), cigars (8.6%) and hookah (7.2%).  7
• In 2015, about half of middle and high school students reported using two or more tobacco products in the past 30 days. 7
• Since 1990, teenagers and young adults have had the highest rates of maternal smoking during pregnancy. In 2014, 10.2% of female teens aged 15 to 19 and 13% of women aged 20 to 24 smoked during pregnancy. 8
• In 2014, around 62% of current smokers in both middle and high school seriously thought about quitting. Seventy percent of middle school and 59%  of high school current smokers had tried to quit smoking for one day or more. 9

Additional Facts about Tobacco Use among Children and Teenagers

• Menthol cigarette use is more common among younger and newer teen smokers.  10 This is due to young smokers perceiving menthol cigarettes as less harsh and easier to smoke.  11
• One study found that teens exposed to the greatest amount of smoking in movies were 2.6 times more likely to start smoking themselves compared with teens who watched the least amount of smoking in movies.  12
• The good news is that help is available for teen smokers who want to quit. The American Lung Association’s Not-On-Tobacco (N-O-T) program is designed for 14- to 19-year-old smokers who want to quit. Contact your local American Lung Association office to find out if N-O-T is available in your area.

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

U.S. Department of Health and Human Services. Preventing Tobacco Use Among Young People: A Report of the Surgeon General, 1994

Substance Abuse and Mental Health Services Administration. National Survey on Drug Use and Health, 2014. Analysis by the American Lung Association Epidemiology and Statistics Unit Using SPSS Software.

Substance Abuse and Mental Health Services Administration. 2015 National Survey on Drug Use and Health: Detailed Tables. 2016.

Centers for Disease Control and Prevention. Office on Smoking and Health. Sustaining State Programs for Tobacco Control: State Data Highlights, 2006. Accessed on June 9, 2008.

American Legacy Foundation. 2000. National Youth Tobacco Survey. 2001.

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

Centers for Disease Control and Prevention. Tobacco Use Among Middle and High School Students — United States, 2011–2015. Morbidity and Mortality Weekly Report. April 15, 2016; 65(14):361-7.

Centers for Disease Control and Prevention. National Center for Health Statistics. CDC WONDER On-line Database, Natality public-use data 2007-2014, 2016.

Centers for Disease Control and Prevention. National Youth Tobacco Survey, 2014. Analysis by the American Lung Association Epidemiology and Statistics Unit using SPSS software.

Hersey JC, Nonnemaker JM, Homsi G. Menthol Cigarettes Contribute to the Appeal and Addiction Potential of Smoking for Youth. Nicotine & Tobacco Research. 2010; 12(Suppl 2):S136–S146.

Klausner K. Menthol Cigarettes and Smoking Initiation: A Tobacco Industry Perspective. Tobacco Control. 2011; 20(Supp 2):ii12–ii19.

Lee YO, Glantz SA. Putting the Pieces Together. Tobacco Control. 2011; 20(Suppl 2):ii1–ii7.

Sargent JD et al. Exposure to Movie Smoking: Its Relations to Smoking Initiation Among US Adolescents. Pediatrics. November 5, 2005; 116(5):1183-91.

Page last updated: June 7, 2024

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Smoking and Its Effects on Human Body Essay

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Introduction

Works cited.

Smoking is one of the oldest traditions followed by millions of people in the world. Despite pleasure and positive feelings, smoking is dangerous as it harms the human body and tissues. Smoking is dangerous as it leads to health-related problems including cardiovascular disease.

According to Carr (22), one-third of all deaths in America are caused by coronary heart disease, and at least 30 percent of these deaths are related to smoking. Smoking affects the lungs and respiratory organs causing such terrible diseases as cancer.

Among the most wider spread diseases are peptic ulcers, cancer of the larynx, kidney, pancreas, and other major organs. The resins from the smoke enter the blood and ruin cells. This process is inevitable if a person smokes for years. Also, smoking harms the fetus, increasing the risk of spontaneous abortion and low birth weight.

The investigators explain the effects of smoking on the breath as follows: the rapid pulse rate of smokers decreases the stroke volume during rest since the venous return is not affected and the ventricles lose the habit and ability to make large strokes.

Similar conditions arise during strenuous exercise, that is, with the rapid heart rate, the diastolic filling is incomplete and the stroke output remains small. This results in a relatively small unit circulation and oxygen supply to the tissues with the result that an oxygen debt must be incurred. This ends in breathlessness and dyspnœa. Just giving the facts is not enough. Attitudes and behaviors need to be addressed (Rabin and Sugarman, p. 43).

Students want behavioral tips on how to maintain peer acceptability while avoiding the pressure to show how cool they are b smoking. While cigarette ads on television and radio are forbidden, “gifts” of cigarettes to minors (particularly in minority communities) are not discouraged as an advertisement ploy.

Moreover, the interlacing of beer ads with sports events and wine cooler ads with upscale women’s television programming sends strong messages to young people about how society views substance use. Role-plays, debates, “raps,” and antismoking jingles allow students an active exploration of their motivation for wanting or not wanting to smoke. These techniques encourage youngsters to think for themselves, to develop their judgments and attitudes (Carr, p. 87).

Recently, studies by Rabin and Sugarman (2003) have demonstrated an increased cancer risk in adulthood among children who were exposed to parental smoking in their early years. An overview of the health effects of passive smoking on children and adults is the same as on active smokers. Smoking has direct physiological effects on the body, and the cumulative wear and tear on the system caused by recurring stress can eventually cause damage to the system. Indeed, there is abundant evidence that stress can cause several physiological and biochemical changes (Cnossen, p. 31).

In sum, smoking harms the human body ruining healthy cells and tissues. Smoking is dangerous as it leads to inevitable changes in blood and tissues of the heart and lungs. Smoking can cause neural and endocrine change that alters the normal functioning of the organism (e.g., change in cardiovascular activity or immune system functioning). This physiological stress response is accompanied by behavioral responses as well. Smoking and the subsequent behavioral response to it can affect health and facilitate, if not cause, some illnesses.

Carr, A. The Easy Way to Stop Smoking: Join the Millions Who Have Become Non-Smokers Using Allen Carr’s Easyway Method. Sterling; 1 edition, 2005.

Cnossen, S. Theory and Practice of Excise Taxation: Smoking, Drinking, Gambling, Polluting, and Driving. Oxford University Press, 2005.

Rabin, R. L., Sugarman, S.D. Regulating Tobacco. Oxford University Press, 2001.

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IvyPanda. (2021, October 19). Smoking and Its Effects on Human Body. https://ivypanda.com/essays/smoking-and-its-effects-on-human-body/

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Article Contents

Introduction, conflict of interest statement.

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The impact of school smoking policies and student perceptions of enforcement on school smoking prevalence and location of smoking

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C. Y. Lovato, C. M. Sabiston, V. Hadd, C. I. J. Nykiforuk, H. S. Campbell, The impact of school smoking policies and student perceptions of enforcement on school smoking prevalence and location of smoking, Health Education Research , Volume 22, Issue 6, December 2007, Pages 782–793, https://doi.org/10.1093/her/cyl102

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The purpose of this study was to comprehensively assess the impact of school tobacco policy intention, implementation and students' perceptions of policy enforcement on smoking rates and location of tobacco use during the school day. Data were obtained from all students in Grades 10–11 ( n = 22 318) in 81 randomly selected schools from five Canadian provinces. Policy intention was assessed by coding written school tobacco policies. School administrators most familiar with the tobacco policy completed a survey to assess policy implementation. Results revealed policy intention and implementation subscales did not significantly predict school smoking prevalence but resulted in moderate prediction of tobacco use on school property ( R 2 = 0.21–0.27). Students' perceptions of policy enforcement significantly predicted school smoking prevalence ( R 2 = 0.36) and location of tobacco use ( R 2 = 0.23–0.63). The research findings emphasize: (i) the need to consider both written policy intention and actual policy implementation and (ii) the existence of a policy is not effective in controlling tobacco use unless the policy is implemented and is perceived to be strongly enforced.

School-based strategies are a key element in adolescent tobacco control because school environments are established systems in which adolescent behavior can be targeted and in which social behaviors are reinforced [ 1 , 2]. School tobacco policies are critical to a comprehensive adolescent tobacco control program, yet research to date shows inconsistencies in the way policies are measured and evaluated. Furthermore, little is known about the most effective strategies linking characteristics of school tobacco policies to adolescent tobacco use.

There has been an increased interest and emphasis on tobacco policies and their impact on youth tobacco use. Studies have shown that school tobacco policies are effective only if they are strongly enforced [ 3 , 4 ]. A comprehensive review of the effects of school policies on youth smoking rates shows inconclusive findings [ 4–11 ]. Some of the ambiguity in strength of relations between policies and smoking behavior is likely a result of the differences in dates when the research was conducted (spanning the 17 years from 1989 to 2006), geography (i.e. Australia, Canada, Scotland and the United States), definitions of smoking behavior used as the outcome variable (i.e. current smoker, daily smoker, occasional smoker, susceptible smoker), sample size, operationalization of tobacco policies (i.e. intention, implementation, enforcement) and conceptualization of tobacco policy strength (i.e. students' versus teachers' perceptions of the policies and/or coded written policies). Based on these observations, it is important to identify consistencies in the way school policies are examined and to determine the relationships among policy intentions, implementation, enforcement and smoking behaviors during adolescence.

Policies that prohibit tobacco use vary in their target application, location of application and timing of enforcement. It is understood that strong policy intention and implementation should include emphases on comprehensiveness, prevention, cessation, punishment, consistency of enforcement, strength and visibility [ 3 , 11–13 ]. The difficulty in exploring distinctions between strength of policy intention and implementation lies in the assessment of written policies with inherent intention to curtail smoking behaviors, and in exploring school informants' strategies to implement written policies. The application of these assessments to research and practice is of value to understanding policy impact.

A written policy can be viewed as a statement of intent addressing tobacco control in the school environment. The application of the written policy is not actualized in practice until the policy is implemented at the school. Furthermore, this actualization may not impact the school environment unless compliance is ensured through enforcement. This level of distinction is often overlooked in policy reviews, yet the use of consistent definitions of policy intention and implementation is critical to the examination of policy content, enforcement and effectiveness. One method of ensuring consistency of policy evaluation is to employ coding rubrics that capture the complexities of both policy intention and implementation. For example, a comprehensive coding rubric for written tobacco policies has been developed to include the main factors critical to strong policies [ 13 ]. The coding system includes five main components: developing, overseeing and communicating the policy, purpose and goals, tobacco-free environments (including prohibition, strength and characteristics of enforcement), tobacco use prevention education and assistance to overcome tobacco addictions. Despite some initial testing of the rubric [ 13 ] it has seen limited use. More importantly, the different roles that school policy components play in influencing adolescent smoking behaviors remains elusive.

In addition to the lack of consistent evaluation strategies associated with written tobacco policies, the other main limitations in research linking policies and smoking behavior include: (i) the dependence on adolescents' perceptions of the strength of school policies; (ii) limited assessments of the consistency between policy intention and implementation and (iii) unclear conceptual links between the tobacco policy variables and youth smoking classifications. Specifically, in research linking tobacco policies and smoking behavior, it is not necessarily that tobacco policies will directly impact whether an individual is a daily or an occasional smoker, or whether they have ever tried a cigarette, but rather they will impact how the school environment contributes to shaping these behaviors. School-based policies should, at minimum, impact the frequency and location of smoking behavior that occurs around and at school. Efforts should be focused on characteristics of smoking behavior that are likely to be influenced, both directly and indirectly, by school-based tobacco policies.

The purpose of this paper is to (i) describe an approach for assessing school smoking policies, (ii) examine differences between school written policy (intention) and reported implementation; (iii) examine characteristics of school written (indention) policies and reported implementation as predictors of school smoking prevalence, as well as smoking prevalence at school, on and off school property and (iv) examine students' perceptions of policy enforcement as predictors of school smoking prevalence.

Participants

Following ethics approvals from the University and Secondary School Districts, a multi-site cross-sectional study was conducted in 81 randomly selected secondary schools from British Columbia, Manitoba, Newfoundland, Ontario and Quebec. The five provinces represent a reasonable geographical balance and have smoking rates that span the range of Canada's overall smoking rate for youth aged 15–19 years (15–24%) [ 14 ].

Following a passive parental consent approach, all students in Grades 10 and 11 ( n = 22 318) within the 81 sampled schools were asked to complete a questionnaire about their smoking attitudes and behavior. From each school, a senior school administrator with extensive knowledge of the tobacco policy was recruited to complete a questionnaire about the implementation of the school smoking policy (i.e. survey on school smoking policies). In the final sample, administrators included principals (50%), vice principals (47.4%), assistant vice principals (1.3%) and teachers (1.3%). Written tobacco policies were also collected from each school and each corresponding school district board for assessment of policy intention.

Data sources and measures

Written school tobacco policies (intention).

To assess policy intention, school policies were collected from administrators, official policy documents or web pages. In the event that schools did not have a written policy, the district policy was obtained since it was the school's official tobacco control document. To assess the strength of policy, the hard copies were coded by two trained researchers using a theoretical and conceptually derived rating scheme [ 13 ]. Modifications to the existing rubric were made to reflect the Canadian context and recent theoretical findings [ 3 , 15 , 16 ]. Several Canadian experts on policy evaluation and implementation were also consulted during this process. Modifications to Stephens and English rubric [ 13 ] involved creating separate subscales for prohibition, strength and characteristics of enforcement instead of using the broader category of ‘tobacco-free environments’. The final rating system was composed of seven policy components that were derived from a number of items: developing, overseeing and communicating the policy; purpose and goals; prohibition; strength of enforcement; characteristics of enforcement; tobacco use prevention education and assistance to overcome tobacco addictions (see Table I for sample items). Two trained researchers used the rating system as a directed assessment instrument to code to the policies. The coders read through the school and district written policies and rated each policy components from ‘poor’ to ‘outstanding’ using a combination of Likert scale and dichotomous response sets. When rating discrepancies occurred, they were discussed until consensus was established.

Sample questions from the policy intention and implementation subscales

School tobacco policy implementation

To assess the implementation of school tobacco policies, it was necessary to develop a structured survey that supported the main policy rubric informing this study. Development of the survey incorporated school health questionnaires [ 17 , 18 ] and guidelines from prominent policy research [ 3 , 13 ]. The survey was pilot tested by three school administrators (not included in our sample) before it was finalized to a total of 41 items (survey can be obtained from first author).

The resulting survey of school smoking policies was completed by school administrators who were knowledgeable about tobacco policies. The responses were coded using the same protocol and scoring system was described for the written school policies (see Table I ). Similar procedures were used to ensure comparability between written policies (intention) and the structured interviews (implementation). The final rating system for policy implementation included an additional subscale for consistency of enforcement.

Student smoking and perceptions of policy enforcement

Student smoking behaviors were assessed using the tobacco module of the School Health Action, Planning and Evaluation System (SHAPES) [ 19 ] which is a research-supported machine readable survey designed to collect perceptions related to multiple characteristics of youth tobacco use [ 19 ]. For this study, questions pertaining to frequency and quantity of tobacco consumption were used to define smoking behavior. A smoker was defined as an adolescent who had smoked at least a few puffs of a cigarette on ≥2 days in the last month. The individual data were used to create two dependent variables. First, a composite score assessing smoking status, which was then converted into a school prevalence rate by adding the number of smokers at each of the schools represented, divided by the total students at the school. Second, prevalence of smokers who smoke at school both on and off the property (location of smoking behavior) was calculated. Prevalence of smoking behavior location was calculated as a ratio of smokers indicating they smoked on and/or off school property divided by the total number of smokers at each school.

Items from SHAPES were also used to create five independent variables related to student perceptions of policy enforcement at the school: (i) perception regarding the percentage of students who smoke (10-point Likert scale ranging from 0–10% to 91–100%); (ii) whether there are punishments for smoking on school property (percentage of students reporting ‘true’ and ‘usually true’); (iii) existence of a clear set of tobacco use rules at school (percentage of students reporting ‘true’ and ‘usually true’); (iv) strong sanctions for breaking the tobacco rules (percentage of students reporting ‘true’ and ‘usually true’) and (v) whether students smoke where they are not allowed (four-point Likert scale ranging from none to a lot).

Data analyses

Prior to main analyses, it was important to assess the validity of the instruments used to code the policies. Nine completed surveys assessing both policy intention and implementation were randomly selected and given to six experts in the field of tobacco policy who were asked to rank order them in terms of strength, and to include rationale for their decisions. This rank ordering was compared with the strength of scores generated from the developed rating system as coded by the trained researchers. The survey and the rater's rankings elicited the same policy strength scores 83.4% of the time.

Following the validation of the policy measures, the psychometrics of the policy scale scores and aggregated data from SHAPES (i.e. smoking behaviors and students' perceptions of policy enforcement) were examined. Descriptive analyses were conducted to examine correlations, means and standard deviations (SDs). t -tests were conducted to examine significant mean differences and between policy intention and implementation. The main analyses involved independent multiple linear regressions conducted to examine (i) policy implementation (reported by school administrators), (ii) policy intention (from written policies) and (iii) students' perceptions of policy enforcement, as predictors of school smoking prevalence and smoking behaviors occurring on and off school property during the school day. No simultaneous models including intention, implementation and perceptions of enforcement were conducted due to the limited power resulting from the small sample size. Since there were no theoretical or conceptual reasons to expect differences among the predictors, all policy subscales were entered in step one. For all analyses, the significance level was set at 0.05. Given the number of predictors and sample size, we recognize the possibility of inflated Type I errors. However, the exploratory nature of this study begins to address an existing gap in the literature. It was desirable to use this approach to identify the majority of factors that may influence school smoking rates.

The majority of schools (80%) had their own tobacco policy, with the remaining schools reporting use of the district policy (see Table II ). The policy intention subscales for tobacco use prevention education and assistance to overcome tobacco addiction were indiscriminant across the schools and were not included in the main regression analyses.

Means and SDs for the policy data

The relationships among smoking prevalence and school-based tobacco control policies are presented in Table III . Very few of the policy implementation subscales were related to policy intention. The majority of students' perceptions of policy enforcement items was significantly correlated with the intention and implementation subscales. None of the policy intention and implementation subscales was related to smoking prevalence. However, student perceptions regarding the percentage of smokers attending the school were related to school smoking prevalence. There were many significant relationships among policy intention, implementation and students' perceptions of school tobacco policy enforcement and smoking prevalence (at school) on and off school property.

Correlations among the policy subscales and smoking prevalence

1, school smoking prevalence; 2, off school property smoking prevalence; 3, on school property smoking prevalence; 4–10, policy intention subscales (4, developing, overseeing and communicating policy; 5, purpose and goal; 6, prohibition; 7, strength; 8, characteristics; 9, tobacco use prevention education; 10, assistance to overcome tobacco addiction); 11–17, policy implementation subscales (11, developing, overseeing and communicating policy; 12, purpose and goal; 13, prohibition; 14, strength; 15, characteristics; 16, consistency; 17, tobacco use prevention education; 18, assistance to overcome tobacco addiction); 18, perception of smoking where not allowed; 19, perception of smoking prevalence; 20, prevalence perception that students can be fined; 21, prevalence perception of clear set of rules; 22, prevalence perception consequences of breaking rules.

P < 0.05.

Differences between intention and implementation subscales

To explore the differences in intention and implementation subscales, a number of t -tests were conducted using a Bonferroni technique to protect against Type 1 errors. Implementation subscales were significantly higher than intention subscales: developing, overseeing and communicating policy, t (1, 76) = 22.50, P < 0.001; prohibition, t (1, 76) = 12.79, P < 0.001; strength of enforcement, t (1, 76) = 9.64, P < 0.001; characteristics of enforcement, t (1, 76) = 9.31, P < 0.001; tobacco use and prevention education, t (1, 76) = 3.04, P < 0.001 and assistance to overcome tobacco addiction, t (1, 76) = 10.82, P < 0.001. The subscales for purpose and goals were not significantly different. Policy implementation had an additional subscale (consistency of enforcement) that was not included in comparative analyses.

Predictors of school smoking prevalence and location of smoking behavior

School smoking prevalence.

To examine policy intention and implementation as predictors of school smoking prevalence, a multiple regression was planned. However, the correlations among both policy implementation and intention showed little, if any, relationships and no further analyses were conducted. The model predicting school smoking prevalence from students' perceptions of school policy was significant, F (5, 72) = 7.86, P < 0.001, R 2 = 0.36, with perception of smokers emerging as an independent predictor (see Table IV ).

Regression analyses predicting school smoking prevalence from perceptions of policy enforcement

Smoking location: at school, on school property

To test the hypothesis that schools with weaker policy intention and implementation would have more smokers using tobacco at school, separate regressions were conducted with location of smoking as the dependent variable (see Table V ). For policy intention, the model was significant, F (5, 70) = 5.17, P < 0.05. The policy intention subscales accounted for 27% of the variance in on school property tobacco use, with prohibition, strength and purpose and goals emerging as significant individual predictors. For policy implementation, the model was also significant, F (8, 69) = 2.28, P < 0.05, R 2 = 0.21. The individual significant predictors included prohibition, consistency of enforcement and tobacco use prevention education. Finally, the model examining adolescents' perceptions of policy enforcement predicting smoking on school property was also significant, F (5, 72) = 22.72, P < 0.001, R 2 = 0.62. The significant independent predictors in the model included perceptions that students can be fined, breaking the rules leads to consequences and prevalence of smokers at school.

Regression analyses predicting tobacco use on school property from policy intention (Model 1), policy implementation (Model 2) and perceptions of policy enforcement (Model 3)

Smoking location: at school, off school property

Regressions were also conducted with smoking on school property as the dependent variable (see Table VI ). The first regression included all policy intention subscales as predictors of smoking at school but off property. The model was not significant, F (5, 70) = 0.190, P > 0.05, R 2 = 0.01. The second regression included the policy implementation subscales as predictors of smoking off school property. The model was not significant, F (8, 69) = 1.85, P = 0.09, R 2 = 0.17. Finally, the model exploring adolescents' perceptions of policy enforcement as predictors of smoking off school property was tested. In this model, adolescents' perceptions of more students smoking where they are not allowed and greater perception of smokers at the school were significant independent predictors in the model, F (5, 72) = 4.11, P < 0.001, R 2 = 0.23.

Regression analyses predicting tobacco use off school property from policy intention (Model 1), policy implementation (Model 2) and perceptions of policy enforcement (Model 3)

The coding scheme developed in this study was used to explore school written tobacco policy intention and administrator-reported policy implementation in secondary schools across Canada. Results revealed few of the school policy implementation subscales were related to written policies (intention) and none of the policy subscales was correlated to smoking prevalence. However, there were many significant relationships among policy intention, implementation and students' perceptions of school tobacco policy enforcement and smoking prevalence at school (both on and off school property). Several moderate predictive models exploring characteristics of adolescent smoking behaviors were observed.

Comparing the strength of the policies, implementation scores were significantly higher than intention for all but one subscale (i.e. purpose/goals). In particular, it is encouraging to note that strength and characteristics of enforcement were higher than the intention subscales since tobacco policies have maximum benefit only when they are strongly enforced [ 3 , 4 ]. The differences may be due to the length of time that the written policy has been in place at the school. However, it may also be that administrators were more optimistic in their reporting of implementation; thus this finding should be interpreted with caution. This result highlights the need to assess both policy intention and implementation.

School smoking prevalence was not significantly related to policy intention or implementation. This suggests that school policies do not have direct consistent effects on smoking prevalence. A number of researchers have noted similar relationships among school tobacco use policies and adolescent smoking prevalence [i.e. 5, 8–11]. Conceptually, it is not surprising that a school policy alone fails to impact school smoking prevalence unless part of a comprehensive tobacco control program. The synergistic impact of tobacco control policies, prevention and cessation programs and other forms of tobacco control approaches in the school (such as health education curriculum and anti-smoking campaigns and promotions) are likely to be most influential and require further investigation for the impact on school smoking.

Students' perceptions of policy enforcement were moderately linked to smoking prevalence. In the predictive model, a perception that there were a higher number of smokers at school was the strongest predictor of smoking prevalence. This finding is supported in research using social cognitive frameworks, where observational learning is a strong predictor of behavior [ 20 , 21 ]. These types of modeled behaviors should be considered when policies are developed and enforced. Furthermore, teacher and staff smoking prevalence were not explored in this study but have the potential to impact student smoking through modeling mechanisms and should be included in future studies. There are reports to suggest that teacher smoking during school hours is associated with adolescent smoking [ 22 ]. If schools can reduce the visibility of tobacco use, both by students and teachers, it will likely have positive effects on controlling the prevalence of adolescent smoking behaviors. This is a potent area for developing awareness and influencing behavior.

None of the school policy intention or implementation subscales significantly predicted smoking prevalence off school property. This finding was expected since school policies likely have more influence on smoking behaviors exhibited on school property. Alternatively, schools with written policies (intention) describing low prohibition, greater strength of enforcement and clearly established purpose and goals had higher smoking rates on school property. For administrator-reported implementation, low prohibition, greater consistency of enforcement and the absence of tobacco use prevention education were related to high smoking rates on school property. These results suggest that further work is needed to decipher the complex interrelationships among prohibition, enforcement, prevention education and school environment when examining smoking on school property. For example, if schools have designated smoking areas (i.e. smoking pit), the higher strength and consistency of enforcement of tobacco policies may be propagating tobacco use on school property but limiting smoking to specific areas. In this case, further research is needed to examine smoking on school property and the possible covariance associated with designated smoking areas. Additionally, students' knowledge and understanding of their school policy is unknown and may interact with smoking behavior. It would be beneficial to assess students' recall and recognition of school-based tobacco policies and to control for these effects when examining smoking behavior.

Students' perceptions of policy enforcement were strongly predictive of smoking on school property. Specifically, in schools where punishment was not perceived a consequence of smoking, there were more students smoking on school grounds. This finding is consistent with previous research [ 5 , 7 ] and highlights the importance of communicating the consequences of breaking the tobacco policy rules. We also found that schools where adolescents' perceived more student smokers had high smoking rates on school property. This finding may again illustrate the power of observational learning and highlights the importance of reducing the visibility and awareness of adolescent smoking behaviors at school.

There are a number of limitations associated with this study. First, the coding rubrics and surveys developed for this project need to be further tested for their reliability and validity. Also, confirmatory factor analyses are needed to examine the factor structure of tobacco policies. The cross-sectional design and relatively small sample of schools in this study preclude more advanced analyses and limit the power to detect statistical results. Future research should explore the longitudinal relationships among tobacco policies and characteristics of smoking behaviors during adolescence.

Despite its limitations, this study makes important contributions to research on school tobacco policies. The conceptualization of policies as intention and implementation, coupled with the development of a coding paradigm used to assess policies, begins to address the need for consistent definition and evaluation of tobacco policies. A cost-effective and practical approach to measure policy implementation reliably is a challenge. Developing student items for cross-validation with administrator reports may be one approach to addressing this challenge. Future studies should continue to move beyond simple classification of smokers and non-smokers and examine conceptually plausible relationships between policy and different smoking behaviors. There are many more opportunities for intervention when we consider location and timing of tobacco use among student smokers at school. In conclusion, exploring the differences between policy intention and implementation highlights the need to further examine inconsistencies in the way tobacco policies are written and the strategies used to implement them. As practitioners, it is important to focus on bridging this gap to ensure consistent and clearly communicated tobacco use prohibition strategies.

None declared.

This research was supported by a Canadian Institutes of Health Research grant no. 62748. We are pleased to acknowledge the contribution of Helen Hsu and Jane Shen who assisted in reviewing the literature. Technical assistance was also provided by Tamiza Abji and Sarah Lockman. Above all, we thank the high school administrators and students who completed the surveys. The research presented in this article is part of a larger study which along with school-based smoking policies is examining the relationship between adolescent tobacco use, school-based programs and factors in the community environment.

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• Published: 07 March 2023

Cigarettes smoking and e-cigarettes using among university students: a cross-section survey in Guangzhou, China, 2021

• Hongjia Song 1 , 2 ,
• Xuemin Yang 3 ,
• Wanchun Yang 1 , 2 ,
• Yuxing Dai 1 , 2 ,
• Kun Duan 3 ,
• Xingtao Jiang 3 ,
• Guangye Huang 1 , 2 ,
• Min Li 1 , 2 ,
• Guoping Zhong 1 , 2 ,
• Peiqing Liu 1 , 2 &
• Jianwen Chen 1 , 2  

BMC Public Health volume  23 , Article number:  438 ( 2023 ) Cite this article

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There is an increase in the use of cigarettes and e-cigarettes worldwide, and the similar trends may be observed in young adults. Since 2014, e-cigarettes have become the most commonly used nicotine products among young adults (Sun et al., JAMA Netw Open 4:e2118788, 2021). With the increase in e-cigarette use and the decrease in use of cigarettes and other tobacco products, however, there is limited information about Chinese smokers, e-cigarettes users and trends in cigarettes and e-cigarettes use among university students. Therefore, our objective was to investigate the using status of cigarettes, e-cigarettes and smoking behavior among the students from 7 universities in Guangzhou, China.

Students at 7 different universities in Guangzhou were investigated online in 2021 through a cross-sectional survey. A total of 10,008 students were recruited and after screening, 9361 participants were adopted in our statistics. Descriptive analysis, Chi-square analysis, and multiple logistic regression analysis were used to explore the smoking status and influencing factors.

The average age of the 9361 university students was 22.4 years (SD = 3.6). 58.3% of participants were male. 29.8% of the participants smoked or used e-cigarettes. Among the smokers and users of e-cigarettes, 16.7% were e-cigarettes only users, 35.0% were cigarettes only users, and 48.3% were dual users.

Males were more likely to smoke or use e-cigarettes. Medical students, students from prestigious Chinese universities, and students with higher levels of education were less likely. Students with unhealthy lifestyles (e.g., drinking alcohol frequently, playing video games excessively, staying up late frequently) were more likely to smoke or use e-cigarettes. Emotion can have significant impacts on both cigarettes and e-cigarettes dual users when choosing cigarettes or e-cigarettes to use. More than half of dual users said they would choose cigarettes when they were depressed and e-cigarettes when they were happy.

We identified factors influencing the use of cigarettes and e-cigarettes among university students in Guangzhou, China. Gender, education level background, specialization, lifestyle habits and emotion all influenced the use of cigarettes and e-cigarettes among university students in Guangzhou, China. Male, low education level, from non-prestigious Chinese universities or vocational schools, non-medical specialization, and presence of unhealthy lifestyles were influencing factors for the use of cigarettes and e-cigarettes among university students in Guangzhou and students with these factors were more likely to smoke or use e-cigarettes. Besides, emotions can influence dual users' choice of products.

This study provides more information to better understand young people's preferences for cigarettes and e-cigarettes by elucidating the characteristics of cigarettes and e-cigarettes use, as well as related influencing factors, among university students in Guangzhou. Further research involving more variables connected to the use of cigarettes and e-cigarettes will be required in our future study.

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Introduction

Despite numerous efforts to stop the tobacco epidemic, tobacco smoking is recognized as a major preventable cause of disease worldwide [ 1 ]. The 2021 Global Report on trends in the prevalence of tobacco use 2000–2025, published by the World Health Organization (WHO), states that tobacco use in any form kills and sickens millions of people every year and over 8 million people died from a tobacco-related disease in 2019 [ 2 ]. Smoking and passive smoke (exposure to second-hand smoke) are the key contributors to the mortality of specialization chronic diseases, namely, cardiovascular disease, chronic respiratory disease, and cancer [ 3 ]. The prevalence of current (at least 1 of the last 30 days) cigarettes smoking among Chinese adults reached 27.7% in 2015, making it one of the highest smoking rates in the world [ 4 ]. The health risks of smoking have attracted more and more attention, and smoking on campus has become a serious school and social problem [ 2 , 5 ].

Customers are getting more worried about the physical harm as their awareness of cigarettes' dangers is increased, and they are more encouraged to choose e-cigarettes which are claimed as less harmful and can meet their needs of risk reduction [ 6 ]. Many researches have shown that e-cigarettes, although they cannot be considered safe [ 7 ], may cause less harm to the body than cigarettes [ 8 , 9 , 10 , 11 ]. Some cigarettes smokers are converting to e-cigarettes to avoid the effects of smoking [ 12 ].

E-cigarettes are electronic devices that deliver nicotine to the respiratory system by atomizing an aerosol of smoke containing glycerin, propylene glycol, nicotine and other additives through an electric heating element [ 13 ]. Since e-cigarettes produce much less tar, carbon monoxide, and carcinogenic ingredients such as aldehydes, acids, and phenols, the exclusive use of e-cigarettes among smokers may reduce the number of diseases caused by such ingredients [ 14 , 15 , 16 ]. Studies have shown that cigarettes and e-cigarettes are the most frequently used nicotine products in youth adults in the USA [ 17 , 18 , 19 , 20 ] and probably China. China is the world’s largest consumer of tobacco products and contributes substantially to the global burden of smoking-related diseases [ 21 ]. It is noteworthy that the use of e-cigarettes in China is far less frequent than in some European countries and the United States [ 22 , 21 , 22 , 23 , 24 , 27 ].

However, the health risks of e-cigarettes have not been adequately studied, data on their effects and risks on human body are limited [ 15 , 28 ]. Despite the fact that using e-cigarettes is a worldwide phenomenon [ 29 , 30 ], there is a paucity of data regarding the knowledge and attitude of e-cigarettes users particularly among the young adults in China [ 31 ]. Studies of cigarettes and e-cigarettes use among e-cigarettes consumers are still in their infancy, with most of them being questionnaires about basic consumer information, consumption behavior and preferences. Most survey respondents are European and American e-cigarettes consumers, and there are limited reports on Chinese e-cigarettes consumers' vaping behavior. There is an urgent need to investigate the status quo and influence factors of smoking and using e-cigarettes [ 32 ].

Therefore, we conducted a cross-sectional survey of using cigarettes and e-cigarettes to investigate the smoking behaviors among university students in Guangzhou. One of our research interests was the use of cigarettes and e-cigarettes among university students. Another focus was on the factors that influence the use of cigarettes and e-cigarettes by university students.

Research design and participants

A cross-sectional survey was developed in China that collected data through a self-administered online structured questionnaire from July to December 2021 among undergraduate and graduate students with different disciplinary backgrounds from 7 universities in Guangzhou. In total, 10,008 participants were recruited through WeChat, while 9361 university students completed the questionnaire with a response rate of 93.5%. The online survey was anonymous, and data were encrypted for added security protection. Before entering the online survey system, all participants reviewed and approved the electronic consent page. By prohibiting users with the same IP (Internet Protocol) address from accessing the survey more than once, duplicate entries were avoided. Incomplete surveys were not sent to the system because of a missed response reminder component that alerted participants in real time about incomplete surveys. This investigation was conducted after obtaining the approval of the Ethics Review Committee (IRB), whose approval number is SYSU202108001.

Sociodemographic

Participants self-reported their gender, age, race/ethnicity, levels of education, and monthly living expenses. We also distinguished the university by three types (vocational school, general universities and prestigious universities) including 7 different universities in Guangzhou, China. A separate variable was created to distinguish the specialization of participants (medical specialization or not).

Cigarettes and e-cigarettes use

Respondents to the survey were asked whether they had smoked or used e-cigarettes even once. Those who had ever smoked or used e-cigarettes were asked if they now smoke or use e-cigarettes. We defined current cigarettes or(and) e-cigarettes use as having smoked or(and) used e-cigarettes at least one day in the last 30 days.

Current cigarettes or(and) e-cigarettes users were asked about the age at first use of cigarettes or e-cigarettes and the product of choice for first use (cigarettes or e-cigarettes). Respondents also were asked how long they have been smoking or using e-cigarettes with the possible answers being from within a month to more than ten years. The using product of initiation (cigarettes or e-cigarettes) was asked if the respondent was a dual user.

Regarding the future choices of smokers and e-cigarettes users, the main focus was to examine whether they choose to become cigarettes only users, e-cigarettes only users or dual users in a year.

Lifestyles variables

Previous studies [ 17 , 18 , 33 ] have shown that unhealthy lifestyles such as alcohol abuse, video gaming addiction, and sleep deprivation are strongly associated with smoking or using e-cigarettes in young adults, so we added lifestyle variables to the study. Three common unhealthy lifestyles were distinguished in our questionnaire including drinking alcohol excessively, playing video games frequently and staying up late (falling asleep after 24 o’clock and getting tired next morning) frequently. We defined frequently as more than three times in a week, and excessively as play video games more than 20 h per week.

Related concepts and definitions

In the survey, participants were divided into four types: cigarettes only users (cigarettes smokers who currently do not use e-cigarettes), e-cigarettes only users (e-cigarettes users who currently do not use cigarettes), dual users (those who currently use both cigarettes and e-cigarettes) and non-nicotine users (those who currently do not use cigarettes and e-cigarettes).

The selected Chinese universities were classified according to their academic prominence as prestigious and non-prestigious according to the QS World University Rankings [ 34 ]. Prestigious Chinese universities refer to Sun Yat-sen University and Jinan University in this study. Non-prestigious Chinese universities include Guangzhou University of Chinese Medicine, Southern Medical University and Guangzhou City Polytechnic. Guangzhou Institute of Science and Technology and Guangzhou Huashang University are vocational schools in China.

Statistical analysis

The categorical variables were expressed as the frequency (%), while the continuous variables were presented as mean ± SD. A single sample Kolmogorov–Smirnov test was used to test whether the data conform to normal distribution. Chi square test was used to compare categorical variables, while independent sample t-test and Mann–whitney U test were respectively used to compare the continuous variables with and without normal distribution. An analysis of multiple logistic regression was conducted to explore the relationship between using behavior of cigarettes and e-cigarettes and lifestyle. When multiple comparisons were involved, the Bonferroni method was used to correct for the test level α. All analyses were done using R software. Significant test was a bilateral test and the level of statistical significance was set at P  < 0.05 for all the analyses.

Participant characteristics and status of cigarettes and e-cigarettes use

Table  1 shows characteristics of participants. The final sample was composed of 9361 individuals, providing a response rate of 93.5%. In the full sample of 9361 participants, 58.3% ( n  = 5461) were male and 41.7% ( n  = 3900) were female.

Table 1 shows that 29.8% of students smoke or use e-cigarettes and that among them, the typical patterns is dual use (48.3%) with 35.0% smoking only cigarettes and 16.7% using only e-cigarettes. Among the dual users, 51.2% ( n  = 690) participants started using cigarettes, 34.4% ( n  = 464) participants e-cigarettes, 14.4% ( n  = 193) did not recall the exact order (Fig. 1 ).

The source distribution of cigarettes and e-cigarettes dual users among university students in Guangzhou, China

Factors associated with smoking or using e-cigarettes

Table 2 shows factors associated with smoking or using e-cigarettes. Among e-cigarettes users, females were more likely to choose e-cigarettes compared to males (78.1 vs. 62.8%, P  < 0.05).

In general, medical students have a higher level of knowledges about health [ 16 , 35 ] and it is important to understand their perceptions of e-cigarettes as they need to communicate and interact with patients during their training and later in their careers. Therefore, we divided the specialization into non-medical specialization and medical specialization, using medicine as a criterion.

The prevalence of cigarettes and e-cigarettes was significantly higher among non-medical specialization than medical specialization (32.7% vs. 12.8%, P  < 0.05), and the highest rate of cigarettes and e-cigarettes use was found among law specialization compared to medical specialization (47.2% vs. 12.8%, P  < 0.05), followed by history (46.1% vs. 12.8%) and philosophy (43.8% vs. 12.8%, P  < 0.05).However, there was no difference in the choice of cigarettes or e-cigarettes between non-medical and medical students.

The use of both e-cigarettes and cigarettes was lower in prestigious Chinese universities compared to other types of schools. Students in non-prestigious Chinese universities had the highest rate of cigarettes and e-cigarettes use and a correspondingly higher rate of e-cigarettes use.

Among the participants, undergraduates and vocational school students had the highest rate of cigarettes and e-cigarettes use (32.8% and 31.8%), followed by Ph.D. students (20.2%), while master students had the lowest rate of cigarettes and e-cigarettes use at 9.7%, with a statistically significant difference ( P  < 0.05).

Among them, there was no difference in the distribution of cigarettes and e-cigarettes use among undergraduates and vocational school students, while the rate of cigarettes use among master students was significantly lower than other students ( P  < 0.05), and the rate of e-cigarettes use? was also the lowest.

Lifestyles have significant impacts on the use of cigarettes and e-cigarettes. Compared to those with appropriate lifestyles, students who drank alcohol frequently, played video games excessively, stayed up late frequently, and did all of the above had an increased odds of cigarettes use, e-cigarettes use, and dual use. Multiple logistic regression analyses of cigarettes only users, e-cigarettes only users, and dual users indicated that the using of cigarettes, e-cigarettes and dual use increased 8.1, 6.8 and 10.2 times respectively for those who drank alcohol compared to those who did not drink alcohol; The odds of cigarettes using, e-cigarettes using and dual using were 2.6, 3.2, and 4.7 times higher for gamers compared to non-gamers, respectively; The odds of cigarettes using, e-cigarettes using and dual using increased by 1.3, 1.2 and 2.4 times respectively for those who stayed up late compared to those who did not stay up late. All results are presented in Table 3 .

Table 4 shows that 83.5% ( n  = 1125) of dual users chose using products (whether cigarettes or e-cigarettes) according to their emotional state, while 56.5% ( n  = 761) of dual users chose cigarettes when they are depressed and e-cigarettes when they are happy.

Future choices of smokers and e-cigarettes users

Table 5 shows that e-cigarettes only users and dual users have a stronger intention to quit using their current nicotine product of use than cigarettes only users( P  < 0.05). Figure 2  displays the willingness of cigarettes only users or e-cigarettes only users to try another product (cigarettes or e-cigarettes) among university students in Guangzhou, China. For cigarettes only users, 41.8% ( n  = 408) report that they will not use e-cigarettes in the future, 30.9% ( n  = 301) use both cigarettes and e-cigarettes in the future, and 27.3% ( n  = 266) would give up cigarettes and use e-cigarettes. For e-cigarettes only users, 42.0% ( n  = 195) would give up e-cigarettes and only use cigarettes, 37.5% ( n  = 174) would use both cigarettes and e-cigarettes, and 20.5% ( n  = 95) would not use cigarettes in the future.

Willingness of cigarettes only users or e-cigarettes only users to try another nicotine product among university students in Guangzhou, China

Our findings were consistent with some prior prevalence studies in which males were more likely to smoke than females (males: females = 37.2:7.5) [ 10 , 36 , 35 , 38 ]. However, the gender difference in e-cigarettes were smaller than in cigarettes, which is also consistent with previous research studies [ 39 , 38 , 41 ].

The gender differences in smoking may be attributed to traditional sociocultural influences [ 31 , 32 , 42 , 41 , 44 ]. Habitual thinking suggests that female's smoking is associated with an inappropriate social image. The social circumstances put more pressure on female smokers, whereas, for male smokers, social opinion has a much smaller negative impact than for females, suggesting that the socio-cultural context have an intervening role in smoking.

In addition, we found that the rate of using cigarettes and e-cigarettes was the highest among undergraduates, followed by Ph.D. students, and the lowest was among master students, both for cigarettes, e-cigarettes, and dual use. It indicates that cigarettes and e-cigarettes use was shown as a non-linear relationship with education level, which is consistent with other studies [ 45 ]. This may be due to the fact that undergraduates have less academic stress and more social activities [ 46 ], which are susceptibility factors for cigarettes and e-cigarettes use. A number of studies have shown that there is a significant correlation between smoking and the education level of the smoker, the higher the education level is, the lower the smoking rate is [ 47 , 48 ]. This is because people with a higher level of education level have a higher level of health awareness, and a relatively higher level of awareness of the diseases caused by smoking and harmful results [ 49 , 50 ], and thus have a lower smoking rate, which explains the relatively lower rate of cigarettes and e-cigarettes use among master student s and Ph.D. students. Undergraduate students were more likely to use e-cigarettes, in contrast to master students and Ph.D. students, who had the lowest rates of cigarettes and e-cigarettes use and a greater preference for cigarettes. It has been established that e-cigarettes use shows a non-linear relationship with education level, but the exact reasons for this are unclear and warrant further study [ 51 ]. Our findings displayed that the use of both e-cigarettes and cigarettes was lower in prestigious Chinese universities perhaps due to the widely different circumstances, different management, and different type of student in different universities. In addition, we found that the cigarettes use rate of Ph.D. students is much higher than the e-cigarettes use rate, which is different from the situation of undergraduates and vocational school students. The reasons for this may be that Ph.D. students are older than others and e-cigarettes are emerging products, so many Ph.D. students are used to using cigarettes and are not familiar or are not willing to try e-cigarettes.

Similar to previous surveys, we found that non-medical students have higher rates of cigarettes and e-cigarettes use than medical students [ 52 ]. This may be due to the fact that medical students are more aware of the effects of nicotine on the body after learning extensive knowledge of physiology and pathology [ 16 , 30 , 43 ]. It is noteworthy that, the highest rate of using cigarettes and e-cigarettes was law students. The considerable pressure placed on them in academic performance can explain this result [ 53 ].

A growing body of research indicates that emotion is also one of the influencing factors of smoking and negative emotions can induce smoking [ 54 , 55 ]. Our findings found that the majority of dual users will use cigarettes rather than e-cigarettes when they are depressed. This result may be due to the different experiences of smoking and vaping while there is no related data to illustrate that smoking cigarettes will provide more pleasure in the present.

We also discovered that among all the future choices, dual use is becoming increasingly popular, as the previous study reported [ 56 ]. 51.2% of the dual users started as cigarettes only users, indicating a huge shift of nicotine products using pattern in young adults. Consistent with our findings above, some studies [ 30 , 41 , 57 ] also indicated that cigarettes only users are more likely to try e-cigarettes than non-smokers. However, a study by Sean Esteban McCabe et al. indicated that dual users had the greatest risk for engaging in risk behaviors (including truancy, grade point average <  = C + , binge drinking, alcohol use, marijuana use, illicit drug use and nonmedical Rx drug use) followed by cigarettes only users, e-cigarettes only users, and non-nicotine products users [ 5 ].

Limitations

There are several limitations to this study. First, the source of the sample was university students, whose smoking behaviors may differ from the general population of young adults and may not apply to the group who are not students. Second, the data was not weighted for adjusting biases to non-equal probability of selection, non-coverage, and non-response. Third, these data are self-reported and might be subject to reporting bias. Finally, the study was a cross-sectional study and could not dynamically observe changes in cigarette and e-cigarette use, we were unable to assess causal relationships.

The present study reveals the use rate of cigarettes and e-cigarettes among university students in Guangzhou, China. This study also provides the possible future choices of cigarettes or e-cigarettes users among university students. Our investigation shows that 29.8% of participants reports that they used cigarettes or e-cigarettes. Among them, 16.7% were e-cigarettes only users, 35.0% were cigarettes only users and 48.3%were dual users. 51.2% of the dual users were developed from cigarette only users.

Additionally, this study investigated influencing factors to cigarettes and e-cigarettes use, showing that gender, school, education level, specialization, and lifestyles all had impacts on the use of cigarettes and e-cigarettes among university students in Guangzhou. Students who were male, had low education levels, from non-prestigious Chinese universities or vocational schools, had non-medical specialization, and the presence of inappropriate lifestyles such as drinking and playing video games excessively were more likely to use cigarettes and e-cigarettes. Besides, emotion also can have significant effects on the choice of using cigarettes or e-cigarettes for dual users.

This study elucidates the characteristics of cigarettes and e-cigarettes use and related influencing factors among university students in Guangzhou, providing more information to better understand young people's preferences for cigarettes and e-cigarettes. This cross-section survey offers a perspective for policy makers to develop more guiding industry rules of young adult's cigarettes and e-cigarettes using.

In our future work, further investigations, which take more variables related to cigarettes and e-cigarettes using into account, will need to be undertaken, and more reliable analytical methods must be required.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

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Informed consent was obtained from all subjects. Informed consent was obtained from all subjects and/or their legal guardian(s).

This work was supported by the National Natural Science Foundation of China (31970699), the Guangdong Basic and Applied Basic Research Foundation (2021A1515010766 and 2019A1515011030), the Guang-dong Provincial Key Laboratory of Construction Foundation(2019B030301005), the Key-Area Research and Development Program of Guangdong Province (2020B1111110003), and the National Major Special Projects for the Creation and Manufacture of New Drugs(2019ZX09301104).

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Song, H., Yang, X., Yang, W. et al. Cigarettes smoking and e-cigarettes using among university students: a cross-section survey in Guangzhou, China, 2021. BMC Public Health 23 , 438 (2023). https://doi.org/10.1186/s12889-023-15350-2

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Essay on Smoking for Students and Children in 1100 Words

Here, you will read a persuasive Essay on Smoking for Students and Children in 1100 Words. It includes the meaning of smoking, why people smoke?, its disadvantages, should it banned.

Table of Contents

Introduction (Essay on Smoking-1100 Words)

What is smoking .

In a common word, we can explain that this is a widespread practice in which people burn any object or its substance, and after that, they take its smoke inside the body through the breathing process. It is done in ancient times.

Why people smoke?

There is no single reason for smoking. People smoke because of various reasons. Some studies say that tobacco comprises nicotine in its contents.

Some people say that they get peace of mind relaxation by smoking. Most people start it from teenagers and entire life they cannot leave this habit. 

Disadvantages of Smoking

If our health gets affected because of any dangerous disease, then how can we survive in life? Now also thousands of people are there who are changing daily for smoking and fighting for their survival.  

1. Causes Cancer

2. increase lung disease.

Because of smoking, many lungs-related problems start in the body, and slowly it creates severe diseases in the complete body.

3. Creating Heart Problems

4. creates infertility.

Our reproductive system is essential, and delicate, which easily gets affected because of smoking.

5. The complication in Pregnancy

6. diabetes problems.

Smoking is one reason for developing diabetes in the human body. They say this type two diabetes.

7. Oral Hygiene Problems

Should smoking should be banned.

Ideally, it should be banned and never should be entitled to the farming of tobacco, eat, sell, and production of tobacco-related objects and items. Smoking, whether it is public or personal, all are dangerous to health badly.

For the companies and tobacco producers, the government can make an original plan. We should encourage them to start any other production in place of tobacco item productions. This is not compulsory from the government side to produce cigarettes or related materials.

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Essay Service Examples Health Smoking

Cause and Effect Essay on Smoking

Introduction

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• Cigarette, rolling cigarettes, smoking pipes.
• Cigars, cigarillos, little cigars
• Dissolvable tobacco, smokeless tobacco (snus), snuff
• Electronic cigarette (nicotine delivery system)
• Hookah, shisha

Tobacco in America

How tobacco has spread all around the world, antismoking propaganda, list of diseases that smoking can cause.

• Lung cancer, over 10 other types of cancer, including Colon, Cervix, Liver, Stomach, and Pancreatic Cancer
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• Heart Disease
• Reproductive Effects in Women, Premature, Low Birthweight Babies
• Smoking in an economical way causes costs to increase
• Social habits, for example, deprivation of senses, dullness, anxiety, stress, and smoking play a major role in causing all these things.
• Smoking causes stress, so it is bad for both types of people smoking and non-smoking
• There is a constant replacement of old smokers with smokers originating in the young generation. Smoking habits have the tendency to spread out of the smoking population over the non-smokers.

Comparison of attitudes towards smoking in different countries

United states, why people shouldn't start smoking how hard is it to quit, how to quit.

• Remind yourself about the benefits of being a non-smoking person, such as improvements in health, being able to run for longer distances, being not addicted to anything, having a better smell, and saving money.
• Nicotine replacement products such as prescription nicotine in a nasal spray or inhaler, nicotine patches, gum, and lozenges
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• Chew something instead of smoking, take gum or candy instead of the cigarette
• Never say 'That is the last one If you quit, then you quit without any excuses
• Start a new activity, for example sport to keep yourself busy
• Surround yourself with people who have a healthy lifestyle and can support you

How to reduce the number of smoking people in the world?

• Secondhand smoke is deadly. When people smoke tobacco products like cigarettes, bidis, and water pipes, second-hand smoke fills enclosed spaces and affects other people.
• Health alerts with pictures are effective.
• Large pictorial or graphic health signs, including clear packaging, and strong messages can inspire smokers to protect the health of nonsmokers by not smoking in the home, improve compliance with smoke-free legislation, and encourage more people to quit smoking. Campaigns in the mass media will also help to curb cigarette demand by encouraging nonsmoker safety and persuading people to quit smoking.
• Ban tobacco advertising.
• Make taxes on tobacco much higher, so people would think it is too expensive to smoke.
• Illegal trade of tobacco products must be stopped, people who sell tobacco to children under age 18 must be fined or sentenced.
• https:www.cancer.orghealthystay-away-from-tobacco why-people-start-using-tobacco.html
• https:en.Wikipedia.orgwikiList_of_smoking_bans
• https:en.Wikipedia.orgwikiList_of_smoking_bans_in_the_United_States
• https:www.fda.govtobacco-products health-information cigarette-smoking-risk-factor-type-2-diabetes
• https:www.cdc.govtobaccobasic_informationhealth_effectspregnancyindex.htm
• https:kidshealth.orgenteenssmoking-asthma.html
• https:www.stroke.org.uksitesdefaultfilessmoking_and_the_risk_of_stroke.pdf
• https:www.cdc.govtobaccodata_statisticssgr50th-anniversarypdfsfs_smoking_CVD_508.pdf
• https:www.lung.orglung-health-diseases lung-disease-lookupcopdwhat-causes-code
• https:www.mayoclinic.orgdiseases-conditions lung-cancer symptoms-causes-20374620
• https:worldpopulationreview.com country-rankings smoking-rates-by-country
• https:www.cancercouncil.com.aucancer-preventionsmokingarticlesa-brief-history-of-smoking
• https:www.mayoclinic.orghealthy-lifestyle quit-smoking in-depth nicotine-craving-20045454
• https:www.who.intnews-room fact-sheets detail tobacco

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Concurrent Drinking and Smoking among College Students: An Event-Level Analysis

Katie witkiewitz.

1 Washington State University - Vancouver

Sruti A. Desai

2 University of Washington

Gillian Steckler

Kristina m. jackson.

3 Brown University

Sarah Bowen

Barbara c. leigh, mary e. larimer.

Katie Witkiewitz and Gillian Steckler are in the Department of Psychology at Washington State University - Vancouver. Sruti Desai and Mary Larimer are at the Center for the Study of Health and Risk Behaviors in the Department of Psychiatry and Behavioral Sciences at the University of Washington. Kristina M. Jackson is at the Center for Alcohol and Addiction Studies in the Department of Community Health at Brown University. Sarah Bowen is in the Department of Psychology at the University of Washington and Barbara C. Leigh is at the Alcohol and Drug Abuse Institute at the University of Washington.

Cigarette smoking and drinking commonly co-occur among college students, a population that is at high risk for developing alcohol and nicotine use disorders. Several studies have been conducted that have examined predictors of drinking or smoking to gain a better understanding of the antecedents of engaging in these behaviors. Yet, few studies have examined specific factors that influence concurrent smoking and drinking in this population. The current study used data from a 21-day electronic diary-based study of college students ( n = 86) who engaged in concurrent drinking and smoking to examine event-level associations between alcohol use and cigarette smoking in the student’s natural environment. We specifically focused on within-person analyses of contexts in which students reported smoking and drinking simultaneously in comparison to contexts in which students reporting drinking without smoking. Situational contexts included environmental setting, whether s/he was alone or with others, and changes in stress and urges to smoke before initiating drinking. Results indicated that students drank more while smoking and smoked three times as many cigarettes, on average, during drinking episodes. Being with others at a party or a bar was associated with increased odds of smoking while drinking. Likewise, increased stress since the prior assessment predicted a greater likelihood of smoking while drinking. Based on the findings from the present study, it is important for future prevention and intervention efforts to consider social settings and heightened stress among students as potential risk factors for engaging in concurrent drinking and smoking.

Cigarette smoking is strongly associated with alcohol use in young adults, particularly those attending college who are beginning to experiment with smoking ( Harrison, Hinson, & McKee, 2009 ; Reed, Wang, Shillington, Clapp, & Lang, 2007 ). A large body of work has examined predictors of drinking and of smoking to gain a better understanding of the antecedents of engaging in drinking or smoking. However, few studies have examined the specific factors that influence concurrent smoking and drinking among college students. The current study was designed to examine contextual correlates of concurrent drinking and smoking among college students using ecological momentary assessment (EMA; Stone & Shiffman, 1994 ), a method that can capture fine-grained information about behavior in the student’s natural environment.

The co-occurrence of smoking and drinking among college students has been shown in many studies (e.g., Dierker et al., 2006 ; Jackson, Colby, & Sher, 2010 ; Reed et al., 2007 ; Weitzman & Chen, 2005 ). Using a nationally representative sample, Weitzman and Chen (2005) found 98% of student smokers drank alcohol. College student smokers drink significantly more per occasion, drink more frequently, and have significantly more alcohol-related problems than non-smoking student drinkers ( Reed et al., 2007 ; Wetter et al., 2004 ). Jackson and colleagues (2010) used a daily web survey and found that drinking and smoking tended to co-occur and that students drank significantly more drinks per day when smoking and smoked significantly more cigarettes when drinking. Unfortunately, the daily surveys did not provide information about the contexts in which concurrent drinking and smoking occurred in their sample.

Contextual risk factors are defined as those environmental characteristics (e.g., a party) and/or individual experiences (e.g., stress) that facilitate drinking and/or smoking. Parties, drinking/smoking with others (versus alone), and sporting events are all associated with higher rates of drinking and smoking ( Colder et al., 2006 ; Etcheverry & Agnew, 2008 ; Grossbard, et al., 2007 ; Piasecki, McCarthy, Fiore, & Baker, 2008 ; Stromberg et al., 2007 ). Recent EMA studies of smoking or drinking in college students have found that being outside, being in the presence of other smokers, and being in a location where smoking was permitted were the strongest predictors of smoking ( Cronk & Piasecki, 2010 ). Thus, several EMA studies have provided information about the contextual influences on smoking (e.g., Cronk & Piasecki, 2010 ) and drinking (e.g., Mohr et al. 2005 ) in the daily lives of college students. However, few studies have evaluated the contextual influences on concurrent drinking and smoking episodes.

Understanding the factors that predict concurrent heavy drinking and smoking may provide valuable information for efforts to reduce smoking-related and drinking-related morbidity and mortality. For example, among nicotine-dependent drinkers, it has been shown that drinking is a predictor of smoking relapse (e.g., Borland, 1990 ). Drinking has been associated with increases in cigarette craving and subsequent increased risk of smoking ( Piasecki et al., 2008 ), perhaps due to cueing, given the evidence that alcohol may act as a cue for tobacco use in both laboratory (Gulliver et al., 1995) and field settings (Shiffman et al., 1994). It has been proposed that nicotine and ethanol may stimulate the same dopaminergic pathways and might result in cravings for one another (Wise, 1988). Cigarette craving has been shown to increase during drinking among social smokers ( King & Epstein, 2005 ). In general, individuals may develop a learned association between smoking and drinking (e.g., smoking while at a bar).

Importantly, programs that target concurrent smoking and drinking may have the net effect of reducing population-level smoking and drinking. Further, decoupling smoking and drinking is an important goal due to findings that concurrent smoking and drinking has been associated with a heightened risk for cancer and neurocognitive deficits, as well as an attenuation of the cardiovascular benefits of drinking alcohol (e.g., Pelucchi, Gallus, Garavello, Bosetti, & La Vecchia, 2006 ), with an estimated 50% increase in health risk when the behaviors are combined, in comparison to the sum of their independent risks (Bien & Burge, 1991).

Using data from a 21-day prospective electronic diary-based study, we examined the event-level associations between alcohol use and cigarette smoking in a college population. We specifically focused on within-person and between-person analyses of contexts in which the individual was smoking and drinking simultaneously in comparison to contexts in which the student drank and did not smoke. Situational contexts included environmental setting, whether s/he was alone or with others, and changes in stress and urges to smoke before initiating drinking. The current study focused on two primary research questions. First, we were interested in replicating the finding that individuals tend to smoke more cigarettes during drinking versus non-drinking episodes and drink more alcohol when smoking (e.g., Jackson et al., 2010 ). The second question was whether contextual factors predicted smoking cigarettes during drinking episodes, as compared to episodes of drinking without smoking. Finally, the effects of contextual factors on both drinking and smoking might vary across gender and level of smoking (daily vs. non-daily smoker; e.g., Cronk & Piasecki, 2010 ; Todd, 2004 ). Thus, we were interested in whether the effects of contextual factors were moderated by gender or daily smoking status.

Participants in the current study were college students enrolled at a large public university in the northwest of the United States. Eligibility criteria for study participants included drinking more than 5/4 drinks per drinking occasion (men/women), at least once in the past month, and drinking alcohol and smoking concurrently at least once per week. Participants were recruited through flyers, advertisements, and email invitations (to students who had been screened as concurrent drinkers/smokers from another ongoing study) and asked to complete an online screening survey. Students who completed the survey were entered into a drawing for a $50 gift certificate. Participants who met inclusion criteria (69%, n =111 of 160 screened) were routed to an online baseline assessment for which they received $20 to complete. Of those, 108 completed the baseline assessment, and 86 enrolled in the daily monitoring study. Reasons for not enrolling included: not coming into our offices for a training session ( n = 20), lack of time for monitoring ( n = 1), and quitting smoking between baseline and the training session ( n = 1).

After baseline, participants attended in-person training on how to complete daily diaries via a web-enabled phone. Beginning the next day, participants were prompted randomly via email or text message to complete a survey three times per day for 21 consecutive days. Participants had two hours to complete each random survey and were sent one reminder within one hour of the initial prompt. Over 75% of reports were completed within 5 minutes after the initial prompt. They were also instructed to complete a survey during each drinking or smoking episode. For both random prompts and participant-initiated reports, drinking and smoking within an episode were defined as the number of drinks and/or cigarettes since the prior report (either via random prompt or self-initiated survey). Students received $3 for each random report, plus a $21 bonus for every week they completed at least two of three random surveys per day. In total, participants could earn $252 for the monitoring period (see response rates below).

Measures of gender and daily smoking status (assessed by the question “Do you smoke every day?”) were collected in the baseline survey. The other measures were derived from the random and event reports. At each report, the students were asked the number of drinking occasions since the prior report (time and day of prior report was shown to the student), how many drinks they consumed per occasion, how many cigarettes they smoked since the prior report, and whether they smoked cigarettes while drinking. From these items we created the outcome measures used in the current study: any drinking/smoking, number of drinks/cigarettes, and concurrent smoking while drinking (yes/no).

Contextual variables assessed at each report included where they were drinking during each occasion (if drinking was reported), including at home, at a party, at a bar 1 , at a restaurant, at a sporting event, outside, or other, and whether they were drinking alone or with others. In addition, single item measures of stress (“Since the last report have you dealt with anything stressful?” rated on a 5 point scale from “not at all” to “very much”) and urges to smoke (“I have a strong urge for a cigarette right now” rated on a 5 point scale from “strongly disagree” to “strongly agree”), adapted from a prior EMA study ( Muraven, Collins, Shiffman, & Paty, 2005 ) and the Questionnaire of Smoking Urges ( Cox, Tiffany, & Christen, 2001 ), were used as predictors of drinking and smoking episodes. Specifically, we calculated the within-person difference in stress and urge ratings between reports to assess whether changes in stress and urges over time impacted subsequent drinking and smoking episodes.

The mean age of the sample was 20.1 (SD=1.7) and 42.1% were female. The sample was 77.4% White, 13.4% Asian, 2.3% American Indian/Alaskan Native, 1.9% African American, 1.1% Native Hawaiian/Pacific Islander, 3.8% “unknown,” and 3.5% of the sample identified as Hispanic or Latino. Consistent with the screening criteria, 4.2% drank alcohol and smoked cigarettes concurrently every day, 51.4% drank alcohol and smoked at the same time a few times per week, and 44.4% drank and smoked at the same time at least once per week. Average number of drinks per drinking day was 5.74 (SD = 2.20), average number of cigarettes per smoking day was 3.08 (SD = 2.77), and 28.1% of the sample were daily smokers.

Over the 21 days of monitoring for 86 participants, 86.2% of random reports (4670/5418) were obtained and 51 participants (59.4% of the sample) also completed 217 participant-initiated reports at times of drinking (68 reports), smoking (96 reports), or drinking and smoking (53 reports). Preliminary analyses revealed no significant differences in drinking/smoking rates or any other variables of interest across random and event reports, nor between those participants who did and did not provide event reports. Given these findings, data from random and event reports were combined for all analyses. The total number of reports was 4887, including 864 occasions of drinking (17.7% of reports), 1706 occasions of smoking (34.9% of reports), and 383 occasions of concurrent drinking and smoking (7.8% of reports). Smoking occurred on 44.3% of all drinking occasions. Daily smokers and females were more likely to smoke cigarettes during drinking episodes than non-daily smokers (χ2 (1, N = 86) =44.18, p <0.001) and males (χ2 (1, N =86)=5.99, p =0.02), respectively. Daily smokers reported smoking while drinking across 60% of drinking occasions versus 47% of non-drinking occasions. Non-daily smokers reported smoking while drinking across 37% of drinking occasions versus 15% of non-drinking occasions.

The average number of drinks consumed while smoking was 5.16 (SD = 3.68), as compared to an average of 4.05 (SD = 3.57) drinks consumed while not smoking. The average number of cigarettes smoked during periods in which drinking occurred was 3.35 ( SD = 3.12), as compared to an average of 0.85 (SD = 1.78) while not drinking. Participants who reported concurrent drinking and smoking ( n = 70, 81.4%) were asked at what point during the drinking episode they started to smoke. Participants responded that they started smoking after starting to drink on 54.9% of occasions, started smoking before starting to drink on 23.9% of occasions, and started smoking and drinking at the same time on 21.2% of occasions.

Bivariate associations between drinking and smoking are provided in Table 1 . The between-subjects correlations (above the diagonal), which were estimated by taking the mean of all occasions averaged across people, indicated strong direct associations between all measures of drinking and smoking. The magnitude of the correlations at the within-subjects level (below the diagonal), which takes into account occasions nested within people, were consistent with the between-subjects associations. The intraclass correlations (ICCs) indicated that 7% of the variability in drinking and 34% of the variability in any smoking was within-person variability.

Between-Subjects (Above Diagonal; N=86), Within-Subjects (Below Diagonal; N=4,887), and Intraclass Correlations among Measures of Drinking and Smoking

Given that occasions of drinking and smoking were nested within students we used multilevel modeling ( Raudenbush & Bryk, 2002 ) to account for the dependence of observations. Specifically, multilevel models provide estimates of the variability within each person (Level 1) and between individuals (Level 2) across occasions. All models were estimated in Mplus version 5.21 ( Muthén & Muthén, 2009 ) using maximum likelihood estimation, which allowed for missing observations and varying numbers of occasions within and between individuals. A logit link function was used for models with binary outcomes (e.g., any drinking) and a log link function was used for models with count outcomes (e.g., number of drinks). Level 1 variables were centered within person and Level 2 variables were grandmean centered.

A series of four models were estimated in an attempt to replicate the analyses conducted by Jackson and colleagues (2010) : (1) drinking predicting any smoking; (2) drinking predicting number of cigarettes; (3) smoking predicting any drinking; and (4) smoking predicting number of drinks. For all models we included the effect of weekend occasions (defined as Friday, Saturday or Sunday) 2 on Level 1 and we included gender as a Level 2 effect. The results, presented in Table 2 , indicated that any smoking, number of cigarettes, and the weekend were significant predictors of any drinking and number of drinks per occasion. Any drinking and number of drinks, but not weekend, were significant predictors of any smoking and number of cigarettes smoked when smoking. Level 2 estimates were also consistent with prior findings ( Jackson et al, 2010 ) with males drinking significantly more drinks per occasion than females.

Unstandardized Estimates (Standard Errors) from Multilevel Models Predicting Smoking and Drinking Behavior

The second goal of the current study was to examine the contexts in which drinking and smoking tended to co-occur, in comparison to contexts when students only engaged in drinking without smoking 3 . We were specifically interested in four different contextual variables: who the student was with while drinking (alone or with others); where the student was drinking (at home, at a party, or at a bar versus all other locations) 4 ; and whether the student reported an increase in stress or urges to smoke since the report prior to the occasion in which drinking was reported. Number of drinks, weekend, smoking level, and gender were also included as predictors.

Results, shown in Table 3 , indicated that concurrent drinking and smoking occasions can be differentiated from occasions in which individuals drank without smoking. First, being at a party or at a bar was associated with significantly greater odds of smoking while drinking, such that individuals who were at a party were 3.57 times more likely to smoke while drinking and individuals who were at a bar were 2.17 times more likely to smoke while drinking. Increased stress from the prior assessment and number of drinks consumed were both associated with concurrent drinking and smoking occasions, such that individuals who experienced an increase in stress and individuals who consumed more drinks were more likely to smoke while drinking.

Multilevel Models Predicting Concurrent Smoking and Drinking Occasions versus non-Smoking Drinking Occasions

Est. = unstandardized estimate; SE = standard error; OR = odds ratio.

The final goal in the current study was to examine whether results from the models of concurrent smoking and drinking were consistent across gender and levels of smoking (non-daily vs. daily smoker at baseline). Estimates of cross-level interactions using random slopes revealed two significant effects by smoking status and no significant moderation effects by gender (all p > 0.10). A significant interaction between urges to smoke and smoking status in the prediction of concurrent smoking and drinking (B (SE) = 0.38 (0.13), p = 0.005), indicated that daily smokers reported a greater decrease in urges to smoke prior to drinking without smoking, whereas non-daily smokers reported an increase in urges to smoke prior to drinking without smoking. Follow-up analyses indicated that daily smokers reported significantly greater urges to smoke than non-daily smokers (β = 0.44, p < 0.001), regardless of the context. Finally, daily smokers were 6.2 times more likely to smoke while drinking at bars versus other locations, as compared to non-daily smokers who were not significantly more likely to smoke while drinking at bars.

The results from the current study add to prior research on the strong association between drinking and smoking among college students ( Dierker et al., 2006 ; Jackson et al., 2010 ; Reed et al., 2007 ). In the current study, students were significantly more like to smoke more cigarettes when they were drinking, and drink more alcohol per occasion when they were smoking. We also extended recent research by identifying contexts in which students were more likely to smoke while they were drinking. Students who drank at a party were over three times more likely to smoke while drinking. Being at a bar was associated with increased odds of smoking while drinking, particularly among daily smokers. Students were significantly more likely to smoke while drinking if they experienced an increase in stress since the prior report.

The results from the current study are consistent with interviews of college students who indicated that smoking while drinking at parties was socially acceptable ( Nichter, Nichter, Carkogle, Richardson, & TERN, 2010 ) and that smoking played an important role during times of stress ( Nichter, Nichter, Carkoglu, & TERN, 2007 ). Thus, interventions that target smoking might be particularly important in social settings and at times of heightened stress among students. Implementing such interventions in real-time via cell phones in response to fluctuations in stress or changes in environmental contexts may be beneficial. Non-daily smokers reported increased urges to smoke if they did not smoke while drinking, while daily smokers had higher levels of urge to smoke, regardless of the context. Importantly, daily smokers only drank without smoking if they reported a very large decrease in urges to smoke since the prior report and, in general, the daily smokers reported more cigarettes smoked between reports. Meditation-based urge surfing exercises have shown efficacy among college student smokers ( Bowen & Marlatt, 2009 ), and may be beneficial for both daily smokers and non-daily smokers.

The current study had several strengths, including multiple daily assessments and the opportunity to capture the contexts in which smoking and drinking occurred in near real-time. The present study also had limitations. To minimize assessment burden, only a few contextual influences were examined in the current study. Because the design was correlational, it is impossible to disentangle whether being in a context (e.g., at a bar) leads to concurrent drinking and smoking or whether individuals selected environments that permitted drinking and smoking. Finally the current study did not evaluate whether characteristics of the students (e.g., motives, sensation seeking) predicted concurrent drinking and smoking. It may be important to determine whether these factors play a role in order to better tailor prevention and intervention efforts.

Decoupling drinking and smoking is an important target for several reasons. Among many individuals who are trying to quit smoking they find drinking to be a high-risk situation for relapse. Furthermore, for individuals who only smoke when they drink, it is important to determine the situations in which they are most likely to smoke while drinking. Individuals who smoke while drinking are still at greater risk for morbidity and mortality related to smoking (than those who drink without smoking), thus understanding how, why, and when these two health risk behaviors co-occur is an important public health goal.

Acknowledgments

This research was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (AA018336). Special thanks are due to Kim Hodge for her invaluable assistance in executing the procedures of this research.

1 Local ordinance prohibited smoking in bars and restaurants, thus it was assumed that reports of smoking at a bar or restaurant occurred in designated smoking areas, not within the bar or restaurant.

2 Time of day was included in initial analyses, but was not predictive of concurrent drinking/smoking in the current sample. Interestingly the drinking occasions and concurrent drinking and smoking occasions were rather evenly distributed across different times of the day.

3 The contextual questions asked during the random and event prompts limited our analyses to comparing the drinking plus smoking occasions versus the drinking-only occasions, because the questions inquired about whom a person was with and where they were “when they were drinking.” Thus, we do not have data on contextual influences on occasions of smoking, unless the participants reported drinking while smoking.

4 Other drinking locations were assessed, however, being at home, at a party, or at a bar represented 82.2% of all drinking occasions and thus we focused on these contexts in the current analyses. Other locations included being at a restaurant (4.9% of drinking occasions), at a sporting event (1.2% of drinking occasions), outside (4.5% of drinking occasions), and “other” (7.2% of drinking occasions).

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