Annual data on the proportion of adults in Great Britain who smoke cigarettes, cigarette consumption, the proportion who have never smoked cigarettes and the proportion of smokers who have quit by sex and age over time.

As of 2022, around **** million adults in the United States were current cigarette smokers. Although this figure is still high, it is significantly lower compared to previous years. For example, in 2011, there were almost ** million smokers in the United States. Smoking demographics in the U.S. Although smoking in the U.S. has decreased greatly over the past few decades, it is still more common among certain demographics than others. For example, men are more likely to be current cigarette smokers than women, with ** percent of men smoking in 2021, compared to ** percent of women. Furthermore, non-Hispanic whites and non-Hispanic Blacks smoke at higher rates than Hispanics and non-Hispanic Asians, with almost ** percent of non-Hispanic whites smoking in 2022, compared to just under **** percent of non-Hispanic Asians. Certain regions and states also have a higher prevalence of smoking than others, with around ** percent of adults in West Virginia considered current smokers, compared to just *** percent in Utah. The health impacts of smoking The decrease in smoking rates in the United States over the past decades is due to many factors, including policies and regulations limiting cigarette advertising, promotion, and sales, price increases for cigarettes, and widespread awareness among the public of the dangers of smoking. According to the CDC, those who smoke are *** to **** times more likely to develop coronary heart disease and stroke and around ** times more likely to develop lung cancer than nonsmokers. In fact, it is estimated that around ** percent of lung cancer deaths in the United States can be attributed to cigarette smoking, as well as ** percent of larynx cancer deaths. Cigarette smokers are also much more likely to develop chronic obstructive pulmonary disease (COPD), with around ** percent of current smokers in the U.S. living with COPD in 2021, compared to just ***** percent of those who had never smoked.

12.7% of adults in England were smokers in 2022, with the highest rate in the mixed ethnic group (17.0%).

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Adult smoking prevalence in California, males and females aged 18+, starting in 2012. Caution must be used when comparing the percentages of smokers over time as the definition of ‘current smoker’ was broadened in 1996, and the survey methods were changed in 2012. Current cigarette smoking is defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Due to the methodology change in 2012, the Centers for Disease Control and Prevention (CDC) recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time. (For more information, please see the narrative description.) The California Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey of randomly selected adults, which collects information on a wide variety of health-related behaviors and preventive health practices related to the leading causes of death and disability such as cardiovascular disease, cancer, diabetes and injuries. Data are collected monthly from a random sample of the California population aged 18 years and older. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The survey has been conducted since 1984 by the California Department of Public Health in collaboration with the Centers for Disease Control and Prevention (CDC). In 2012, the survey methodology of the California BRFSS changed significantly so that the survey would be more representative of the general population. Several changes were implemented: 1) the survey became dual-frame, with both cell and landline random-digit dial components, 2) residents of college housing were eligible to complete the BRFSS, and 3) raking or iterative proportional fitting was used to calculate the survey weights. Due to these changes, estimates from 1984 – 2011 are not comparable to estimates from 2012 and beyond. Center for Disease Control and Policy (CDC) and recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time.Current cigarette smoking was defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Prior to 1996, the definition of current cigarettes smoking was having smoked at least 100 cigarettes in lifetime and smoking now.

This data shows the percentage of adults (age 18 and over) who are current smokers.

Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs.

Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities.

This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture.

Data source: Public Health England, Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

In 2022, a survey about tobacco dependence in the U.S. found that around 68 percent of smokers had an interest in quitting smoking. However, only around half of adults attempted to quit smoking during the previous year. This statistic displays the percentage of U.S. adult smokers with interest in quitting, a past-year quit attempt, or recent successful cessation as of 2022, by gender.

Crosstab of smoking prevalence and network positions of smokers.

Smoking levels and covariates from the five sample datasets.

Objective: Smoking is a major cause of worldwide morbidity and mortality. Evidence-based intervention programs to help young adults quit smoking are largely lacking; identifying targets for intervention is therefore critical. A candidate target is inhibitory control, with previous studies on Go/No-Go trainings showing behavior change in the food and alcohol domain. The current study examined the mechanisms of change of HitnRun, a Go/No-Go game, in a smoking population that was motivated to quit. Methods: A two-armed experimental study (n = 106) was conducted and young adults (Mage = 22.15; SDage = 2.59) were randomly assigned to either play HitnRun or to read a psychoeducational brochure. Prior to and directly following the intervention period, smoking-specific and general inhibitory control, perceived attractiveness of smoking pictures, and weekly smoking behavior were assessed. Results: Results indicate that Go/No-Go training seems to be effective in decreasing evaluations of smoking stimuli rather than top-down smoking-specific and general control processes. Similar reductions for weekly smoking were found in both groups. Conclusions: We conclude that HitnRun shows some promise, but more research and iterative design is needed to create a multi-component intervention for smoking cessation that is dynamically adjustable to the individual needs of young people.

Average daily cigarette consumption at one-year follow-up by ENDS use and ends use characteristics among non-quitters for all baseline smokers (N = 680*) and baseline daily smokers (N = 543*).

Cigarette smoking among adults including the proportion of people who smoke, their demographic breakdowns, changes over time, and e-cigarettes.

This dataset includes information regarding proportion who smoke cigarettes, cigarette consumption, the proportion who have never smoked cigarettes and proportion of smokers who have quit by sex/age over time in Great Britain from 1974 to 2019.

This data package, titled Data from: Cross-sectional personal network analysis of adult smoking in rural areas, includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file.

Readers should know that this dataset corresponds to the study (paper) Cross-sectional personal network analysis of adult smoking in rural areas.

The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed egos). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county).

Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study.

The variables in the ego_data.rds file are as follows:

(1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation);

(2) "ego_age" (the age of each study participant);

(3) "ego_age.cen" (the age of each study participant, centered);

(4) "ego_educ_b" (the education of each ego, binary);

(5) "ego_educ_f" (the education of each ego, educational achievement);

(6) "ego_marital.s_f" (the marital status of each ego);

(7) "ego_occupation.cat2_f" (the occupation of each ego);

(8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed);

(9) "ego_relstatus_b" (whether the ego is in a relationship or not);

(10) "ego_sex_f" (the sex of the ego assigned at birth; male & female);

(11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female);

(12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others);

(13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others);

(14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others);

(15) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);

(16) "ego_smoking_3cat" (smoking status: non-smoker, former smoker, smoker);

(17) "net.size" (number of social contacts, alters, that were elicited by an ego);

(18) "net.components" (number of strong components in the personal network);

(19) "net.deg.centralization" (personal network degree centralization);

(20) "net.density" (personal network density).

The variables in the alter_data.rds file are as follows:

(1) "alter_age" (the age of the alter);

(2) "alter_age.cen" (the age of the alter - centered);

(3) "alter_btw" (alter's betweenness score);

(4) "alter_btw.cen" (alter's betweenness score - centered);

(5) "alter_deg" (alter's degree score);

(6) "alter_deg.cen" (alter's degree score - centered);

(7) "alter_educ_b" (alter's education);

(8) "alter_educ_f" (alter's education);

(9) "alter_marital.s_f" (alter's marital status);

(10) "alter_relstatus_b" (alter's marital status - binary variable);

(11) "alter_sex_f" (alter's sex assigned at birth);

(12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male);

(13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others);

(14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others);

(15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others);

(16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker);

(17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker);

(18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker);

(19) "assortativity_score_smoker" (assortativity score for alter, smoker);

(20) "ego.alter_meet_f" (ego's meeting frequency with alter);

(21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable);

(22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes);

(23) "alter_rel.w.ego_f" (type of alters in an ego's network);

(24) "networkCanvasUUID" (alpha numeric code for alter);

(25) "networkCanvasEgoUUID" (alpha numeric code for ego);

(26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker);

(27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker);

(28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship);

(29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship);

(30) "ego_type_smk" (smoking egos by type of ego-alter relationship);

(31) "ego_sex_f" (ego's sex, binary);

(32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male);

(33) "ego_educ_b" (ego's education, binary variable)

(34) "ego_age" (ego's age)

(35) "ego_age.cen" (ego's age, centered)

(36) "ego_relstatus_b" (ego's marital status, binary variable)

(37) "ego_occupation_b" (ego's employment status, binary variable)

(38) "net.components" (number of strong components in the personal network)

(39) "net.deg.centralization" (degree centralization score in the personal networ)

(40) "net.density" (density score in the personal network)

(41) "prop_fsmokers" (proportion of former smokers in the personal network - alters)

(42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters)

(43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters)

(44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters)

(45) "prop_smokers" (proportion of smokers in the personal network- alters)

(46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

In 2022, a survey on tobacco dependence in the U.S. found nearly ** percent of the smokers in the Midwest were interested in quitting, while ** percent tried to quit smoking in the past year and *** percent successfully stopped smoking. This statistic displays the percentage of U.S. adult smokers with interest in quitting, a past-year quit attempt, or recent successful cessation as of 2022, by region.

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated 8/14/2024. Adults are defined as 18 years of age and older. The CDC defines a "Current Smoker" as an adult who has smoked at least 100 cigarettes (5 packs) in their lifetime and currently smokes either "Every Day" or "Some Days." BRFSS data methodology changed in 2011; therefore, 2011 and after is not comparable to 2010 data and before.

BackgroundThe potential of electronic nicotine delivery systems (ENDS) to reduce the cardiovascular and other disease risks of smoking is of great interest. While many smokers report using ENDS for cessation, their impact under real-world use patterns and conditions on adult smokers’ quitting behavior is uncertain. The objective of this study was to generate more recent and comprehensive evidence on the effect of “real world” ENDS use on the population quit rates of adult smokers while taking account of frequency and duration of use, device type, e-liquid flavor, and reasons for use.Methods and findingsWe conducted a population-based, prospective cohort study of a random probability sample of 1284 U.S. adult smokers recruited in August/September 2015 and re-contacted one-year later (September 2016) from GfK’s KnowledgePanel, a national, probability-based web-panel designed to be representative of non-institutionalized U.S. adults. Among the 1081 baseline smokers who remained members of KnowledgePanel, 858 completed the follow-up survey. The primary outcome was smoking abstinence for at least 30 days prior to follow-up. Secondary outcomes were making a quit attempt during the 12-month study period and number of cigarettes smoked per day at follow-up. The adjusted odds of quitting smoking were lower for those that used ENDS at baseline (9.4%, 95% CI = 5.22%-16.38%; AOR = 0.30, 95% CI = 0.13–0.72) compared to smokers who did not use at ENDS (18.9%, 95% CI = 14.24%-24.68%). Smokers who used ENDS daily at some point during the study period were also less likely to quit smoking than nonusers (AOR = 0.17; 95% CI = 0.04–0.82). Limited ability to draw causal inferences from the observational design and a lack of biochemical verification of quitting smoking or ENDS use are limitations of this study.ConclusionsWe found no evidence that ENDS use, within context of the 2015–2016 US regulatory and tobacco/vaping market landscape, helped adult smokers quit at rates higher than smokers who did not use these products. Absent any meaningful changes, ENDS use among adult smokers is unlikely to be a sufficient solution to obtaining a meaningful increase in population quit rates. Additional research is needed to reconcile the divergent literature and monitor the impact of ENDS in an environment of rapidly evolving markets and regulatory policies.

Network statistics from the five sample datasets.