This dataset was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors' ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages. The dataset uses a two-stage cluster sample design to produce representative samples of students in middle schools (grades 6–8) and high schools (grades 9–12)

This dataset is valuable for data science due to its coverage of youth tobacco use over nearly two decades. Its rich demographic details and broad geographical spread enable researchers and policymakers to identify trends, behaviors, and risk factors associated with tobacco use among the youth.

For instance, it can help in understanding how tobacco use prevalence varies across different age groups, genders, races, and educational backgrounds. The stratification of data by location and demographic characteristics allows for targeted analysis that can inform public health strategies and educational campaigns aimed at reducing tobacco use among young people.

Some analysis of this dataset can include:

• Statistical assessments of tobacco use trends, examining changes in attitudes towards tobacco, and identifying high-risk groups based on demographic characteristics.
• Performing time-series analyses to understand how tobacco use has evolved over the years or spatial analyses to identify geographical variations in tobacco use trends.
• Correlation studies can help uncover associations between tobacco use and factors like education levels, race, and gender.
• Advanced machine learning models could predict future trends in youth tobacco use or evaluate the potential impact of new tobacco control measures.

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.

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).

Overview This dataset contains synthetic images of port scenes for training and testing port smoke monitoring AI systems. Each image simulates various smoke conditions in various port operating environments, as well as key information such as smoke of different shapes and concentrations. In particular, some smoke effects in rare scenarios are added to the images, such as dense black smoke caused by sudden fires and colored smoke caused by chemical leaks, aiming to challenge the machine learning model's ability to identify and analyze complex smoke conditions. This dataset is very valuable for projects focusing on computer vision, smoke detection and recognition, and port environment simulation. If you want to see more practical application cases of synthetic data in the port field, you can visit www.neurobot.co to schedule a demo, or register to upload personal images to generate customized synthetic data that meets your project needs.

Note Important disclaimer: This dataset is not part of any official port research, nor does it appear in peer-reviewed articles reviewed by port experts or security researchers. It is recommended for educational purposes only. The synthetic smoke and other elements in the images are not generated based on real port data. Do not use them in actual port smoke monitoring production systems without proper review by experts in the field of AI safety and port operation regulations. Please be responsible when using this dataset and fully consider the possible ethical implications.

Historical chart and dataset showing U.S. smoking rate by year from 2000 to 2022.

The male smoking prevalence in Mexico was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the eighth consecutive decreasing year, the male smoking rate is estimated to reach ***** percent and therefore a new minimum in 2029. Shown is the estimated share of the male 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 male smoking prevalence in countries like Canada and United States.

The Excel dataset called ‘Data2’ shows people (uniquely identified by ‘pid), living in households (‘hhid’), who were interviewed in 5 regions in Georgia over 3 waves in a longitudinal survey. The variable prefix shows the wave in which they were interviewed (i.e. ‘w1_’ indicates wave 1, ‘w2_’ indicates wave 2 and ‘w3_’ indicates wave 3).

Short explanation of the data: region: indicates the region in which the respondent lived age: age in years gen: gender b1: Did you give up smoking since the last wave? smk_type: Smoking type of tobacco used

The smoking prevalence in the United States was forecast to continuously decrease between 2024 and 2029 by in total two percentage points. After the eighth consecutive decreasing year, the smoking prevalence is estimated to reach 19.93 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 150 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.

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%.

BackgroundTobacco smoking is a carcinogen for many cancers including bladder cancer. The microbiota is involved in the occurrence, development, and treatment of tumors. We explored the composition of male urinary microbiome and the correlation between tobacco smoking and microbiome in this study.MethodsAlpha diversity, principal component analysis (PCA) and Adonis analysis, linear discriminant analysis (LDA) coupled with effect size measurement, and PICRUSt function predictive analysis were used to compare different microbiome between smokers and non-smokers in men.ResultsThere were 26 qualified samples included in the study. Eleven of them are healthy controls, and the others are from men with bladder cancer. Simpson index and the result of PCA analysis between smokers and non-smokers were not different (P > 0.05) in healthy men. However, the abundance of Bacteroidaceae, Erysipelotrichales, Lachnospiraceae, Bacteroides, and so on in the urinary tract of smokers is much higher than that of non-smokers. Compared to non-smokers, the alpha diversity in smokers was elevated in patients with bladder cancer (P < 0.05). PCA analysis showed a significant difference between smokers and non-smokers (P < 0.001), indicating that tobacco smoking plays a vital role in urinary tract microbial composition.ConclusionThe composition of microbiome in the urinary tract is closely related to tobacco smoking. This phenomenon is more significant in patients with bladder cancer. This indicates tobacco smoking may promote the occurrence and development of bladder cancer by changing urinary tract microbiome.

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;

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:

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

Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches such as we are developing. To begin addressing this need we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response and looked for genes that respond to radiation differently in smokers and non-smokers and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry. Blood from each of 24 different donors was exposed to four doses of ionizing radiation (0 0.1 0.5 or 2 Gy) and analyzed using single-color microarray hybridization. The donors represented equal numbers of male and female smokers (1 or more packs a day) and non-smokers. There are 95 data sets in the study as the sample from one of the female smokers exposed to 2 Gy was lost.

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

This report contains results from an annual survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15). 7,296 pupils in 246 schools completed questionnaires in the autumn term of 2010. The National Centre for Social Research (NatCen) and the National Foundation for Educational Research (NFER) carried out the survey on behalf of The NHS Information Centre for health and social care. The Home Office and The Department for Education also have an interest in the statistics. This is the most recent survey in a series that began in 1982. Each survey since 1998 has included a core set of questions on smoking, drinking and drug use and, since 2000 the remainder of the questions have focused in alternate years on smoking and drinking or on drug use. The emphasis of the 2010 survey is on smoking and drinking whilst still containing some information on drug use. The survey report presents information on the percentage of pupils who have ever smoked, tried alcohol or taken drugs. The report also explores the attitudes and beliefs of school children towards smoking and drinking and from where and from whom children obtain cigarettes and alcohol. Relationships between smoking, drinking and drug use are explored along with the links between smoking, drinking and drug use and other factors such as age, gender, ethnicity and previous truancy or exclusion.

Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches such as we are developing. To begin addressing this need we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response and looked for genes that respond to radiation differently in smokers and non-smokers and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry. Blood from each of 24 different donors was exposed to four doses of ionizing radiation (0 0.1 0.5 or 2 Gy) and analyzed using single-color microarray hybridization. The donors represented equal numbers of male and female smokers (1 or more packs a day) and non-smokers. There are 95 data sets in the study as the sample from one of the female smokers exposed to 2 Gy was lost.

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.

This table contains 6930 series, with data for years 1994 - 1994 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), Age group (10 items: Total; 12 years and over;15-19 years;20-24 years;12-14 years ...), Sex (3 items: Both sexes; Men; Women ...), Characteristics (21 items: Number of smokers in 1994/95;Number of smokers in 1994/95 who did not state their smoking status by 1996/97;Number of smokers in 1994/95 who did not quit by 1996/97;Number of smokers in 1994/95 who quit by 1996/97 ...).