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.

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.

📊

This dataset provides a detailed analysis of smoking trends worldwide, covering essential metrics such as:
- Total smokers and smoking prevalence rates
- Cigarette consumption and brand market share
- Tobacco taxation and smoking ban policies
- Smoking-related deaths and gender-based smoking patterns

Spanning data from 2010 to 2024, this dataset offers valuable insights for health research, policy evaluation, and data-driven decision-making.

📊 Column Descriptions

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).

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) ...

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.

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.

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

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%.

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.

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 ...

Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data was reported at 39.400 % in 2016. This records an increase from the previous number of 39.000 % for 2015. Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data is updated yearly, averaging 37.600 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 39.400 % in 2016 and a record low of 32.900 % in 2000. Indonesia ID: Smoking Prevalence: Total: % of Adults: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank: Health Statistics. Prevalence of smoking is the percentage of men and women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Bangladesh BD: Smoking Prevalence: Males: % of Adults data was reported at 44.700 % in 2016. This records a decrease from the previous number of 45.100 % for 2015. Bangladesh BD: Smoking Prevalence: Males: % of Adults data is updated yearly, averaging 47.000 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 55.900 % in 2000 and a record low of 44.700 % in 2016. Bangladesh BD: Smoking Prevalence: Males: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Prevalence of smoking, male is the percentage of men ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

IntroductionMost people who smoke cigarettes begin in their teens and teens may also be attracted to new tobacco, nicotine, and cannabis products. We describe use prevalence among upper-secondary school students in Switzerland, including daily use, of tobacco, nicotine, and cannabis products.MethodsWe invited secondary school students (age 15 to 21) in two Swiss cantons to take an online survey between October 2021 and February 2022. The survey collected demographic information and asked how frequently they used tobacco products (cigarettes in commercial packages, self-rolled cigarettes, hookahs, pipes, cigars and cigarillos, tobacco heating systems, snus, snuff), non-tobacco nicotine products (nicotine pouches, e-cigarettes with and without nicotine), and cannabis products (smoking with and without tobacco, cannabis vaping). Answers were scored on a Likert scale (no use in past month, less than weekly, weekly but not daily, daily use, prefer not to say), then tabulated and reported as descriptive statistics.ResultsOf 32,614 students in the schools we contacted, 9,515 (29.2%) completed the survey; 49.5% identified as female and 48.4% as male; 9.5% were under 16, 47% were 16–17, 27.5% were 18–19, and 16% were over 19. Reported daily use was most frequent for tobacco cigarettes in commercial packages (14.2%), snus (4.1%) and cannabis smoking with tobacco (3.6%). Most participants (54.8%) reported they had used at least one product at least once within the last month.ConclusionStudents who used a product were most likely to smoke cigarettes, but many regularly used new tobacco, nicotine and cannabis products, though use frequency varies.

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population (CNP) at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Unit (PSU)s and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the CNP at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the CNP at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This "second replenishment sample" was combined for estimation and analysis purposes with the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the CNP at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respondents and augment the analyses of the characteristics of tobacco products used

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).

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

This dataset contains three smoking related indicators.

Rates of self reported four-week smoking quitters

Smoking quit rates per 100,000 available from the HNA.

- These quarterly reports present provisional results from the monitoring of the NHS Stop Smoking Services (NHS SSS) in England. This report includes information on the number of people setting a quit date and the number who successfully quit at the 4 week follow-up. Data for London presented with England comparator. PCT level data available from NHS.

Number of Deaths Attributable to Smoking per 100,000 population by borough

Deaths attributable to smoking, directly age-sex standardised rate for persons aged 35 years +. Causes of death considered to be related to smoking are: various cancers, cardiovascular and respiratory diseases, and diseases of the digestive system.

Numbers of adults smoking by borough

Prevalence of smoking among persons aged 18 years and over.
- Population who currently smoke, are ex-smokers, or never smoked by borough. This includes cigarette, cigar or pipe smokers. Data by age is also provided for London with a UK comparator.

Relevant links: http://www.hscic.gov.uk/Article/1685

http://www.apho.org.uk/default.aspx?QN=HP_DATATABLES

The indicator measures the share of the population aged 15 years and over who report that they currently smoke boxed cigarettes, cigars, cigarillos or a pipe. The data does not include use of other tobacco products such as electronic cigarettes and snuff. The data are collected through a Eurobarometer survey and are based on self-reports during face-to-face interviews in people’s homes.

ABSTRACT Smoking is considered a chronic disease and one of the leading causes of preventable death in the world. The quality of life is an important measure of health impact and its correlation with nicotine dependence levels and smoking is unclear. We evaluated the quality of life of smokers and its correlation with smoke load and the nicotine dependence level. Smokers of both sexes and with no diagnosis of clinical diseases were included in this study. We evaluated their quality of life and level of nicotine dependence through questionnaires. The sample consisted of 48 individuals, 27 women and 21 men. There was a negative correlation between vitality and the amount of years these individuals have smoked (p=0.009;r=-0.27), as well as the general health condition and pack/years (p=0.02; r=-0.23), and the current amount of cigarettes consumed per day (p=0.006;r=-0.29). We can also observe a negative correlation between functional capacity and the Fagerström questionnaire score (p=0.004;r=-0.3). We concluded that the smoke load and the nicotine dependence levels were related to worse quality of life indices of the smoking population.