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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.

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.

📊

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

U.S. Department of Health and Human Services (HHS). Centers for Disease Control and Prevention (CDC). Healthy People 2020 Tobacco Use Objectives. Healthy People 2020. Healthy People 2020 provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. This dataset includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020.

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.

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.

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.

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 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

Healthy People 2020 dataset provides a framework for action to reduce tobacco use to the point that it is no longer a public health problem for the Nation. This dataset includes information related to the Healthy People 2020 Tobacco Use objectives, operational definitions, baselines, and targets. Baseline years may vary by objective. Targets represented correspond to the year 2020.

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).

This dataset contains the data on 144 daily smokers each rating 44 preparatory activities for quitting smoking (e.g., envisioning one's desired future self after quitting smoking, tracking one's smoking behavior, learning about progressive muscle relaxation) on their perceived ease/difficulty and required completion time. Since becoming more physically active can make it easier to quit smoking, some activities were also about becoming more physically active (e.g., tracking one's physical activity behavior, learning about what physical activity is recommended, envisioning one's desired future self after becoming more physically active). Moreover, participants provided a free-text response on what makes some activities more difficult than others.

Study

The data was gathered during a study on the online crowdsourcing platform Prolific between 6 September and 16 November 2022. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 2338).

In this study, daily smokers who were contemplating or preparing to quit smoking first filled in a prescreening questionnaire and were then invited to a repertory grid study if they passed the prescreening. In the repertory grid study, participants were asked to divide sets of 3 preparatory activities for quitting smoking into two subgroups. Afterward, they rated all preparatory activities on the perceived ease of doing them and the perceived required time to do them. Participants also provided a free-text response on what makes some activities more difficult than others.

The study was pre-registered in the Open Science Framework (OSF): https://osf.io/cax6f. This pre-registration describes the study setup, measures, etc. Note that this dataset contains only part of the collected data: the data related to studying the perceived difficulty of preparatory activities.

The file "Preparatory_Activity_Formulations.xlsx" contains the formulations of the 44 preparatory activities used in this study.

Data

This dataset contains three types of data:

- Data from participants' Prolific profiles. This includes, for example, the age, gender, weekly exercise amount, and smoking frequency.

- Data from a prescreening questionnaire. This includes, for example, the stage of change for quitting smoking and whether people previously tried to quit smoking.

- Data from the repertory grid study. This includes the ratings of the 44 activities on ease and required time as well as the free-text responses on what makes some activities more difficult than others.

There is for each data file a file that explains each data column. For example, the file "prolific_profile_data_explanation.xlsx" contains the column explanations for the data gathered from participants' Prolific profiles.

Each data file contains a column called "rand_id" that can be used to link the data from the data files.

In the case of questions, please contact Nele Albers (n.albers@tudelft.nl) or Willem-Paul Brinkman (w.p.brinkman@tudelft.nl).

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.

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

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.

** Description**

This dataset contains data about lung cancer Mortality and is a comprehensive collection of patient information, specifically focused on individuals diagnosed with cancer. This dataset contains comprehensive information on 800,000 individuals related to lung cancer diagnosis, treatment, and outcomes. With 16 well-structured columns. This large-scale dataset is designed to aid researchers, data scientists, and healthcare professionals in studying patterns, building predictive models, and enhancing early detection and treatment strategies.

🌍 The Societal Impact of Lung Cancer

Lung cancer is not just a disease — it's a global crisis that steals time, health, and hope from millions of people every year. As the #1 cause of cancer deaths worldwide, it takes more lives annually than breast, colon, and prostate cancer combined.

But behind every statistic is a story:

A parent who never saw their child graduate.

A worker who had to leave their job too soon.

A community that lost a leader, a friend, a neighbor.

Why does this matter? Lung cancer often goes undetected until it's too late. It’s aggressive, silent, and devastating — especially in underserved areas where early detection is rare and treatment options are limited. It doesn’t just affect patients. It affects families, economies, and healthcare systems on a massive scale.

This dataset represents more than numbers. It represents 800,000 real-world stories — people who can help us unlock patterns, train models, and advance life-saving research.

By working with this data, you're not just analyzing a dataset — you're stepping into the fight against one of humanity’s deadliest diseases.

Let’s turn insight into impact. (😊The above descriptions is generated with the help of AI, Just wanted to share this dataset That all. Thank you)

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

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) ...

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.