The smoking prevalence in the United States was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the ****** consecutive decreasing year, the smoking prevalence is estimated to reach ***** percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like Canada and Mexico.

Behavioral Risk Factors - Tobacco

2011-2015 BRFSS Survey Data

By Health [source]

About this dataset

The Centers for Disease Control and Prevention (CDC) developed the State Tobacco Activities Tracking and Evaluation (STATE) System to monitor modifiable behavioral risk factors of chronic diseases and other leading causes of death. Specifically, this dataset focuses on tobacco topics such as cigarette smoking status, cigarette smoking prevalence according to demographics, cigarette smoking frequency, and quit attempts from BRFSS surveys from participating states across the United States.

This dataset includes columns such as Year, LocationAbbr, LocationDesc, TopicType, TopicDesc, MeasureDesc DataSource Response Data_Value_Unit Data_Value_Type Data_Value_Footnote_Symbol Data_Value_Std _Err Sample-Size Gender Race Age Education GeoLocation DisplayOrder which record the information collected by state BRFSS surveys. The collection of data is extremely important in understanding trends in tobacco use across different races gender education levels locations etc which results in more effective public health interventions aimed at reducing harm caused by cigarette use

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How to use the dataset

This dataset contains information about modifiable risk factors for chronic diseases and other leading causes of death related to cigarette smoking. It can be used to identify trends in tobacco-related behavioral risks, as well as analyze the different associated factors.

In order to use this dataset effectively, it is important to understand the columns and what each represents: ⦁ Year – This column shows the year of the survey data. ⦁ LocationAbbr – Abbreviation of the location from which the data originates.
⦁ LocationDesc – Description of the location from which the data originated.
⦁ TopicType - Type of topic that is being examined in regards to tobacco-related behavior. ⦁ TopicDesc - Description of the specific topic being examined in terms of cigarette smoking behavior risks.
⦁ MeasureDesc - Further description provided on how a measure was identified or calculated for each topic/question asked in relation to cigarette smoking behaviors and risk factors studied.
⦁ DataSource - Source(s) where responses were collected when applicable (e.g., interviews, mailed questionnaires).
⦁ Response – The response associated with a given measure when applicable (e.g., “yes” or “no”). ⦁ Data_Value_Unit– The unit used by any numeric measures given, such as percentages or percents (%)

Research Ideas

• Creating an online smoking cessation program to educate people on the long-term effects of smoking and the risks associated with it.
• Investigating differences in smoking habits by demographic factors such as race, gender, education level, age and location in order to plan public health interventions that most effectively target high-risk populations.
• Developing a mobile app that tracks cigarette consumption levels over time, allowing users to monitor their progress towards quitting and/or reductions in their nicotine addiction

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------| | YEAR | Year of the survey (Integer) | | LocationAbbr | Abbreviation of the location (String) | | LocationDesc | Description of the location (String) | | TopicType | Type of topic (String) | | TopicDesc | Description of the topic (String) | | MeasureDesc | Description of the measure (String) | | DataSource | Source of the data (String) | | Response | Response to the survey question (String) | | Data_Value_Unit | Unit of the data value (String) | | Data_Value_Type | Type of the data value (String) | | Data_Value_Footnote_Symbol | Symbol of the data value footnote (String) ...

US States Tobacco Use Prevalence

1996-2010 Survey Data

By Health [source]

About this dataset

This dataset from the Centers for Disease Control and Prevention (CDC) provides state-based surveillance information related to tobacco use among American adults from 1996 to 2010. It contains data on modifiable risk factors for chronic diseases and other leading causes of death obtained from annual BRFSS surveys conducted in participating states.

The dataset focuses on key topics such as cigarette smoking status, prevalence by demographics, frequency, and quit attempts. The metrics collected are important indicators of public health efforts in tobacco prevention, control and cessation programs at the state level.

With this dataset you can explore how people perceive smoking differently across geographical areas as well as their socio-economic backgrounds like gender identity, race or ethnicity, educational level or life stage. Analyzing this data will give us valuable insights into the impact of tobacco consumption in our society today and help create more effective public health interventions tailored to local needs

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How to use the dataset

This dataset can be used to study the prevalence of tobacco use in different US states in the period 1996-2010. The dataset contains information on cigarette smoking status, prevalence by demographics, frequency, and quit attempts.

In order to begin exploring this dataset it is recommended that one first understand the column headers and their corresponding values. This can be done by familiarizing oneself with the included data dictionary that defines each column's name and description.

Next it is recommended to familiarize oneself with the data types contained in the columns. Depending on what type of query you are wanting to make some columns may need conversion from one type to another for better results when performing a query. Some common types found within this dataset include integers (whole numbers), strings (text) and floats (decimals).

Once you have familiarized yourself with both the columns and data types it is now a good time to start considering which questions you want answer related to tobacco use in US states during this period of time. Consider which variables might provide valuable insights into your analysis such as age, gender, race etc., as well as other variables such as location or year that could add more complexity or context understanding into your analysis. Assuming that your desired questions have been determined you can begin querying your data using methods supported by whichever language or platform you are choosing work with such us SQL or Python Pandas Dataframes etc.. This will allow manipulation of all relevant variables according get useful insights out of them related back tobaccos use in US states during this specific period.

Finally when doing an analysis on any given topic its helpful no compare ones findings between multiple datasets if possible so consider obtaining any other datasets relevant top toxins use over a similar timespan which could be compared against these findings if available

Research Ideas

• Identifying and targeting high-risk locations for tobacco use prevention efforts by analyzing the prevalence of different forms of tobacco use in different states.
• Examining patterns of tobacco use among different demographic groups (gender, age, race, etc.) to design better tailored interventions for tobacco cessation.
• Comparing quit attempt rates with smoking frequency and prevalence across states to understand the effectiveness of smoke-free laws and policies that have been enacted in recent years

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------| | YEAR | Year of survey (Integer) | | LocationAbbr | Abbreviation of the state (String) | | LocationDesc | Full name of the state (String) | | TopicType | Type of topic (String) | | TopicDesc | Description of the topic (String) | | MeasureDesc | Description of ...

Comparing the *** selected regions regarding the smoking prevalence , Myanmar is leading the ranking (***** percent) and is followed by Serbia with ***** percent. At the other end of the spectrum is Ghana with **** percent, indicating a difference of ***** percentage points to Myanmar. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Adults included in this indicator are current cigarette smokers. Current smokers are defined as adults who smoked at least 100 cigarettes in their lifetime and currently smoke.Tobacco use is a leading preventable cause of premature death and disability. Cities and communities can curb tobacco use by adopting policies to regulate tobacco retail and reduce exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The dataset contains 1120 images divided equally into two classes, where 560 images are of Smoking (smokers) and remaining 560 images belong to NotSmoking (non-smokers) class. The dataset is curated by scanning through various search engines by entering multiple keywords that include cigarette smoking, smoker, person, coughing, taking inhaler, person on the phone, drinking water etc. We tried to consider versatile images in both classes for creating a certain degree of inter-class confusion in order to better train the model. For instance, Smoking class contains images of smokers from multiple angles and various gestures. Moreover, the images in NotSmoking class consists of images of non-smokers with slightly similar gestures as that of smoking images such as people drinking water, using inhaler, holding the mobile phone, coughing etc. The dataset can be used by the prospective researchers to propose deep learning algorithms for automated detection and screening of smoker towards ensuring the green environment and performing surveillance in smart cities. All images in the dataset are preprocessed and resized to a resolution of 250×250. We considered 80% of the data for training and validation purposes and 20% for the testing.

Please cite this article if you use this dataset in your research: A. Khan, S. Khan, B. Hassan, and Z. Zheng, “CNN-Based Smoker Classification and Detection in Smart City Application,” Sensors, vol. 22, no. 3, pp. 892, 2022.

Most Americans recognize that smoking causes serious diseases, yet many Americans continue to smoke. One possible explanation for this paradox is that perhaps Americans do not accurately perceive the extent to which smoking increases the probability of adverse health outcomes. This paper examines the accuracy of Americans’ perceptions of the absolute risk, attributable risk, and relative risk of lung cancer, and assesses which of these beliefs drive Americans’ smoking behavior. Using data from three national surveys, statistical analyses were performed by comparing means, medians, and distributions, and by employing Generalized Additive Models. Perceptions of relative risk were associated as expected with smoking onset and smoking cessation, whereas perceptions of absolute risk and attributable risk were not. Additionally, the relation of relative risk with smoking status was stronger among people who held their risk perceptions with more certainty. Most current smokers, former smokers, and never-smokers considerably underestimated the relative risk of smoking. If, as this paper suggests, people naturally think about the health consequences of smoking in terms of relative risk, smoking rates might be reduced if public understanding of the relative risks of smoking were more accurate and people held those beliefs with more confidence.

project use R for graph :

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Tobacco smoking stands as a significant global health crisis, affecting millions worldwide and leading to severe health complications and premature deaths. This issue has persisted for decades, with an estimated 100 million people succumbing prematurely due to smoking-related causes throughout the 20th century, predominantly in affluent nations. However, a decline in the global smoking rate signals a positive shift in global health, potentially enabling millions to enjoy longer, healthier lives.

Annually, smoking is responsible for approximately 8 million premature deaths. These figures highlight the urgent need for effective measures to combat this epidemic. The World Health Organization (WHO) and the Institute for Health Metrics and Evaluation (IHME) provide critical data on the mortality rates associated with tobacco use, emphasizing the gravity of the situation. According to the latest WHO estimates as of November 2023, over 8 million people die each year due to tobacco use, with more than 7 million of these deaths directly linked to smoking. Additionally, around 1.3 million nonsmokers die from exposure to second-hand smoke. The IHME's Global Burden of Disease study further supports these findings, estimating that 8.7 million deaths annually can be attributed to tobacco use, including 7.7 million from smoking and 1.3 million from second-hand smoke exposure, alongside an additional 56,000 deaths from chewing tobacco.

The impact of smoking on mortality is disproportionately higher among men, who account for 71% of premature deaths due to smoking. This disparity underscores the need for targeted interventions that address the specific risks and behaviors associated with smoking among different demographics.

Understanding the vast death toll from tobacco use requires a comprehensive approach that encompasses all forms of tobacco consumption, including smoking and chewing tobacco. The data indicate that the vast majority of tobacco-related deaths are due to smoking, with figures from the IHME suggesting that smoking-related deaths constitute more than 99.9% of all tobacco-use deaths. This emphasizes the critical importance of focusing public health efforts on reducing smoking rates to mitigate the overall impact of tobacco on global health.

The interactive charts and studies provided by organizations like the WHO and IHME offer valuable insights into the global and regional dynamics of smoking-related health issues. These resources allow for a detailed examination of smoking trends and their health consequences, facilitating evidence-based policy-making and public health strategies aimed at reducing smoking prevalence and its associated health burden.

Efforts to combat smoking must take into account the various factors that contribute to its prevalence, including societal norms, economic factors, and the addictive nature of nicotine. Public health campaigns, legislative measures, and support programs for those trying to quit smoking are essential components of a comprehensive strategy to address this issue.

Furthermore, research into the health effects of smoking and the mechanisms by which it contributes to diseases such as cancer, heart disease, and respiratory illnesses is crucial for developing effective treatments and prevention strategies. By understanding the full scope of smoking's impact on health, researchers and policymakers can better target interventions to reduce smoking rates and improve public health outcomes.

In conclusion, the global health crisis posed by tobacco smoking is a multifaceted issue that requires concerted efforts from governments, public health organizations, and communities worldwide. The declining trend in smoking rates offers hope, but the continued high prevalence of smoking-related deaths underscores the need for ongoing action. Through research, public health initiatives, and policy interventions, it is possible to further reduce smoking rates and alleviate the tremendous health burden it imposes on societies around the globe.

The information is from the "National Health Interview Survey" of the Ministry of Health and Welfare, which collects information on smoking behavior from the public through telephone interviews. For more information, please visit the "Tobacco Hazard Prevention Information Website" of the National Health Administration (http://tobacco.hpa.gov.tw/).The definition of "daily smoking rate" is the ratio of individuals who have smoked more than 100 cigarettes from the past to present and have used tobacco daily in the last 30 days. The formula for calculation is: Number of respondents aged 15 and above who answered "smoked more than 100 cigarettes so far" and "used tobacco daily in the last 30 days" / Number of valid completed interviews of individuals aged 15 and above * 100%.

Annual data on the proportion of adults in Great Britain who use e-cigarettes, by different characteristics such as age, sex and cigarette smoking status.

The global number of smokers in was forecast to continuously increase between 2024 and 2029 by in total **** million individuals (+**** percent). After the ******** consecutive increasing year, the number of smokers is estimated to reach *** billion individuals and therefore a new peak in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smokers in countries like Caribbean and Africa.

This report contains results from the latest survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15), focusing on smoking, drinking and drug use. It covers a range of topics including prevalence, habits, attitudes, and wellbeing. This survey is usually run every two years, however, due to the impact that the Covid pandemic had on school opening and attendance, it was not possible to run the survey as initially planned in 2020; instead it was delivered in the 2021 school year. In 2021 additional questions were also included relating to the impact of Covid. They covered how pupil's took part in school learning in the last school year (September 2020 to July 2021), and how often pupil's met other people outside of school and home. Results of analysis covering these questions have been presented within parts of the report and associated data tables. It includes this summary report showing key findings, excel tables with more detailed outcomes, technical appendices and a data quality statement. An anonymised record level file of the underlying data on which users can carry out their own analysis will be made available via the UK Data Service later in 2022 (see link below).

Smoking is so common, and feels so familiar, that it can be hard to grasp just how large the impact is. Every year, around 8 million people die prematurely as a result of smoking.1 This means that about one in seven deaths worldwide are due to smoking.2 Millions more live in poor health because of it.

Smoking primarily contributes to early deaths through heart diseases and cancers. Globally, more than one in five cancer deaths are attributed to smoking.

This means tobacco kills more people every day than terrorism kills in a year.

Smoking is a particularly large problem in high-income countries. There, cigarette smoking is the most important cause of preventable disease and death. This is especially true for men: they account for almost three-quarters of deaths from smoking.

The impact of smoking is devastating on the individual level. In case you need some motivation to stop smoking: The life expectancy of those who smoke regularly is about 10 years lower than that of non-smokers.

It’s also devastating on the aggregate level. In the past 30 years more than 200 million have died from smoking. Looking into the future, epidemiologists Prabhat Jha and Richard Peto estimate that “If current smoking patterns persist, tobacco will kill about 1 billion people this century.”

It is on us to prevent this.

Number and percentage of persons being current smokers, by age group and sex.

Dataset

This dataset was created by Han Lee

Released under CC BY-NC-SA 4.0

Contents

When using any of this project's data, please reference the correct peer-reviewed publication listed below. Please see the publication for additional details on how the measures are constructed. Where multiple versions of the data exist, we recommend using the most recent version for new projects.E-cigarette Taxes: E-cigarette tax scheme vary across states and localities, making comparisons across states difficult. This project provides standardized e-cigarette tax rates at the state and local levels in the United States. 2nd Edition:Publication: Cotti, Chad, Erik Nesson, Michael F. Pesko, and Serena Phillips. "Standardising the measurement of e-cigarette taxes in the USA (2nd edition), 2010–2023 ." Tobacco control (2024).PubMed Link: https://pubmed.ncbi.nlm.nih.gov/39580153/Download: E-cig Tax Version 2, 2010-2023.xlsxDescription: The downloadable data file includes 2 tabs:Closed System E-cigarette Taxes by State/County from 2010 to 2023, 35% Retailer Markup, Time-Invariant Tax UnitsOpen System E-cigarette Taxes by State/County from 2010 to 2023, 35% Retailer Markup, Time-Invariant Tax Units 1st Edition:Publication: Cotti, Chad, Erik Nesson, Michael F. Pesko, Serena Phillips, and Nathan Tefft. "Standardising the measurement of e-cigarette taxes in the USA, 2010–2020." Tobacco control 32, no. e2 (2023): e251-e254.PubMed Link: https://pubmed.ncbi.nlm.nih.gov/34911814/Download: E-cig Tax Version 1, 2010-2020.xlsxDescription: The downloadable Excel file includes 3 tabs:E-cigarette Taxes by State/County from 2010 to 2020, 35% Retailer Markup, Time-Invariant Tax UnitsE-cigarette Taxes by State/County from 2010 to 2020, 20% Retailer Markup, Time-Invariant Tax UnitsE-cigarette Taxes by State/County from 2010 to 2020, 35% Retailer Markup, Time-Varying Tax UnitsIndoor Air LawsThis database reports US national- and state-level estimates of population coverage of comprehensive and partial indoor smoking restrictions from 1990 to 2021 for bars, restaurants, and workplaces, and comprehensive indoor vaping restrictions from 2006 to 2021 for the same locations. Estimates were calculated by using policy data from the American Nonsmokers' Rights Foundation. 1st Edition:Publication: Seidenberg, Andrew B., Karl Braganza, Maxwell Chomas, Megan C. Diaz, Abigail S. Friedman, Serena Phillips, and Michael Pesko. "Coverage of Indoor Smoking and Vaping Restrictions in the US, 1990-2021." American Journal of Preventive Medicine (2024).67, no. 4 (2024): 494-502.PubMed Link: https://pubmed.ncbi.nlm.nih.gov/38876294/Download: Vaping and Smoking Indoor Air Laws Version 1, 2010-2021.xlsxFlavored Tobacco Product Sales Restrictions:This longitudinal dataset describes state and national population coverage and comprehensiveness of flavored tobacco sales from 2010 to 2023 for e-cigarettes, cigarettes, cigars, and smokeless tobacco. Comprehensiveness considers retailer and product exemptions.1st Edition:Publication: Seidenberg, Andrew B., Karl Braganza, Maxwell Chomas, Megan C. Diaz, Abigail S. Friedman, Serena Phillips, and Michael Pesko. "Population Coverage of Flavored Tobacco Sales Restrictions in the United States, 2010–2023." Tobacco Control (2025).PubMed Link: https://pubmed.ncbi.nlm.nih.gov/41115799/Download: Flavored Tobacco Product Sales Restrictions Version 1 - 2010-2023.xlsxResearch reported in this project was supported by the National Institute On Drug Abuse of the National Institutes of Health under Award Number R01DA045016. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Contains a set of data tables for each part of the Smoking, Drinking and Drug Use among Young People in England, 2021 report

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

2008-2024. American Lung Association. Cessation Coverage. Medicaid data compiled by the Centers for Disease Control and Prevention’s Office on Smoking and Health were obtained from the State Tobacco Cessation Coverage Database, developed and administered by the American Lung Association. Data from 2008-2012 are reported on an annual basis; beginning in 2013 data are reported on a quarterly basis. Data include state-level information on Medicaid coverage of approved medications by the Food and Drug Administration (FDA) for tobacco cessation treatment; types of counseling recommended by the Public Health Service (PHS) and barriers to accessing cessation treatment. Note: Section 2502 of the Patient Protection and Affordable Care Act requires all state Medicaid programs to cover all FDA-approved tobacco cessation medications as of January 1, 2014. However, states are currently in the process of modifying their coverage to come into compliance with this requirement. Data in the STATE System on Medicaid coverage of tobacco cessation medications reflect evidence of coverage that is found in documentable sources, and may not yet reflect medications covered under this requirement.

IntroductionPromoting smoking cessation is a global public health priority. E-cigarettes are increasingly being used by individuals to try quitting smoking. Identifying sources and types of information available to adults who are trying to quit, and the impact of this information during a quit attempt, is critical to augment the potential public health benefit of e-cigarettes for reducing cigarette smoking.MethodsUS adults (N = 857) who reported using e-cigarettes in a recent smoking cessation attempt completed an anonymous, cross sectional, online survey. We examined sources of information and type of information received when using e-cigarettes to quit smoking and their associations with the duration of abstinence achieved.ResultsThe two most commonly reported information sources were friends (43.9%) and the internet (35.2%), while 14.0% received information from a healthcare provider. People received information on type of device (48.5%), flavor (46.3%), and nicotine concentration (43.6%). More people received information about gradually switching from smoking to vaping (46.7%) than abruptly switching (30.2%). Obtaining information from healthcare providers (β (SE) = 0.16 (0.08), p = 0.04), getting information about abruptly switching to e-cigarettes (β (SE) = 0.14 (0.06), p = 0.01) and what nicotine concentrations to use (β (SE) = 0.18 (0.05), p = 0.03) were associated with longer quit durations.ConclusionsAmidst the growing popularity of e-cigarettes use for quitting smoking, our results highlight common sources of information and types of information received by individuals. Few people received information from healthcare providers indicating a gap in cessation support that can be filled. Providing information about immediate switching to e-cigarettes and nicotine concentrations to use may help in increasing quit rates and duration.