This dataset contains information of excise taxes paid by distributors that sell e-cigarettes.

Update Frequency : Daily

Introduction

The Smoking Event Detection (SED) and the Free-living Smoking Event Detection (SED-FL) datasets were created by the Multimedia Understanding Group towards the investigation of smoking behavior, both while smoking and in-the-wild. Both datasets contain the triaxial acceleration and orientation velocity signals ( DoF) that originate from a commercial smartwatch (Mobvoi TicWatch E™). The SED dataset consists of (20) smoking sessions provided by (11) unique subjects, while the SED-FL dataset contains (10) all-day recordings provided by (7) unique subjects.

In addition, the start and end moments of each puff cycle are annotated throughout the SED dataset.

Description

SED

A total of (11) subjects were recorded while smoking a cigarette at interior or exterior areas. The total duration of the (20) sessions sums up to (161) minutes, with a mean duration of (8.08) minutes. Each participant was free to smoke naturally, with the only limitation being to not swap the cigarette between hands during the smoking session. Prior to the recording, the participant was asked to wear the smartwatch to the hand that he typically uses in his everyday life to smoke. A camera was already set facing the participant, including at least the whole length of the arms in its field of view. The purpose of video recording was to obtain ground truth information for each of the puff cycles that occur during the smoking session. Participants were also asked to perform a clapping hand movement both at the start and end of the meal, for synchronization purposes (as this movement is distinctive in the accelerometer signal). No other instructions were given to the participants. It should be noted that the SED dataset does not contain instances of electronic cigarettes (also known as vaping devices), or heated tobacco products.

SED-FL

SED-FL includes (10) in-the-wild sessions that belong to (7) unique subjects. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any smoking session and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their smoking sessions to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the recordings sums up to (78.3) hours, with a mean duration of (7.83) hours.

For both datasets, the accompanying Python script read_dataset.py will visualize the IMU signals and ground truth for each of the recordings. Information on how to execute the Python scripts can be found below.

The script and the daataset's pickle file must be located in the same directory.

Tested with Python 3.6.4

Requirements: Pandas, Pickle and Matplotlib

Visualize signals and ground truth

python read_datasets.py

Annotation

For all recordings, we annotated the start and end points for each puff cycle (i.e., smoking gesture). The annotation process was performed in such a way that the start and end times of each smoking gesture do not overlap each other.

Technical details

SED

We provide the SED dataset as a pickle. The file can be loaded using Python in the following way:

import pickle as pkl import pandas as pd

with open('./SED.pkl','rb') as fh: dataset = pkl.load(fh)

The dataset variable in the snippet above is a dictionary with keys, each corresponding to a unique subject (numbered from to ). It should be mentioned that the subject identifier in SED is in-line with the subject identifier in the SED-FL dataset; i.e., SED’s subject with id equal to is the same person as SED-FL’s subject with id equal to .

The content of a dataset ‘s subject is a list with length equal to corresponding subject’s number of recorded smoking sessions. For example, assuming that subject has recorded smoking sessions, the command:

sessions = dataset['8']

would yield a list of length equal to . Each member of the list is a Pandas DataFrame with dimensions , where is the length of the recording.

The columns of a session’s DataFrame are:

'T': The timestamps in seconds

'AccX': The accelerometer measurements for the axis in (m/s^2)

'AccY': The accelerometer measurements for the axis in (m/s^2)

'AccZ': The accelerometer measurements for the axis in (m/s^2)

'GyrX': The gyroscope measurements for the axis in (rad/s)

'GyrY': The gyroscope measurements for the axis in (rad/s)

'GyrZ': The gyroscope measurements for the axis in (rad/s)

'GT': The manually annotated ground truth for puff cycles

The contents of this DataFrame are essentially the accelerometer and gyroscope sensor streams, resampled at a constant sampling rate of Hz and aligned with each other and with their puff cycle ground truth. All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the SED-FL dataset. The ground truth is a signal with value during puff cycles, and elsewhere.

No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).

SED-FL

Similar to SED, we provide the SED-FL dataset as a pickle. The file can be loaded using Python in the following way:

import pickle as pkl import pandas as pd

with open('./SED-FL.pkl','rb') as fh: dataset = pkl.load(fh)

The dataset variable in the snippet above is a dictionary with keys, each corresponding to a unique subject. It should be mentioned that the subject identifier in SED-FL is in-line with the subject identifier in the SED dataset; i.e., SED-FL’s subject with id equal to is the same person as SED’s subject with id equal to .

The content of a dataset ‘s subject is a list with length equal to corresponding subject’s number of recorded daily sessions. For example, assuming that subject has recorded 2 daily sessions, the command:

sessions = dataset['8']

would yield a list of length equal to (2). Each member of the list is a Pandas DataFrame with dimensions (M \times 8), where (M) is the length of the recording.

The columns of a session’s DataFrame are exactly the same with the ones in the SED dataset. However, the 'GT' column contains ground truth that relates with the smoking sessions during the day (instead of puff cycles in SED).

The contents of this DataFrame are essentially the accelerometer and gyroscope sensor streams, resampled at a constant sampling rate of (50) Hz and aligned with each other and with their smoking session ground truth. All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the SED dataset. The ground truth is a signal with value (+1) during smoking sessions, and (-1) elsewhere.

No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component).

Ethics and funding

Informed consent, including permission for third-party access to anonymized data, was obtained from all subjects prior to their engagement in the study. The work leading to these results has received funding from the EU Commission under Grant Agreement No. 965231, the REBECCA project (H2020).

Contact

Any inquiries regarding the SED and SED-FL datasets should be addressed to:

Mr. Konstantinos KYRITSIS (Electrical & Computer Engineer, PhD candidate)

Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124

Tel: +30 2310 996359, 996365 Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

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

Annual data on the proportion of adults in England 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.

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.

This data set contains the data on basis of which the manuscript ""A cigarette a day keeps the goodies away": Smokers show automatic approach tendencies for smoking - but not for food-related stimuli" was written. Smoking leads to the development of automatic tendencies that promote approach behavior toward smoking-related stimuli which in turn may maintain addictive behavior. The present study examined whether automatic approach tendencies toward smoking-related stimuli can be measured by using an adapted version of the Approach-Avoidance Task (AAT).

Daily cigarette smoking among adults (%)

  Dataset Description

This dataset provides information on 'Daily cigarette smoking among adults' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.

Index:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/daily-cigarette-smoking-among-adultsby-sex-for-african-countries.

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).

Context

This dataset contains the prevalence and trends of tobacco use for 1995-2010. Percentages are weighted to population characteristics. Data are not available if it did not meet Behavioral Risk Factor Surveillance System (BRFSS) stability requirements. For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.

Content

This dataset has 7 variables:

• Year

• State

• Smoke everyday

• Smoke some days

• Former smoker

• Never smoked

• Location 1

Acknowledgements

The original dataset can be found here.

Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].

Inspiration

• How does tobacco use change over time?

• Does tobacco use differ by state?

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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_26dc9d0415ebc42309c8fbcf209d3e07/view

This dataset presents information on the proportion of the population who are currently daily smokers.

Daily smokeless tobacco use among adults (%)

  Dataset Description

This dataset provides information on 'Daily smokeless tobacco use among adults' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/daily-smokeless-tobacco-use-among-adultsby-sex-for-african-countries.

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 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.

Smoking leads to the development of automatic tendencies that promote approach behavior toward smoking-related stimuli which in turn may maintain addictive behavior. The present study examined whether automatic approach tendencies toward smoking-related stimuli can be measured by using an adapted version of the Approach-Avoidance Task (AAT). Given that progression of addictive behavior has been associated with a decreased reactivity of the brain reward system for stimuli signaling natural rewards, we also used the AAT to measure approach behavior toward natural rewarding stimuli in smokers. During the AAT, 92 smokers and 51 non-smokers viewed smoking-related vs. non-smoking-related pictures and pictures of natural rewards (i.e. highly palatable food) vs. neutral pictures. They were instructed to ignore image content and to respond to picture orientation by either pulling or pushing a joystick. Within-group comparisons revealed that smokers showed an automatic approach bias exclusively for smoking-related pictures. Contrary to our expectations, there was no difference in smokers’ and non-smokers’ approach bias for nicotine-related stimuli, indicating that non-smokers also showed approach tendencies for this picture category. Yet, in contrast to non-smokers, smokers did not show an approach bias for food-related pictures. Moreover, self-reported smoking attitude could not predict approach-avoidance behavior toward nicotine-related pictures in smokers or non-smokers. Our findings indicate that the AAT is suited for measuring smoking-related approach tendencies in smokers. Furthermore, we provide evidence for a diminished approach tendency toward food-related stimuli in smokers, suggesting a decreased sensitivity to natural rewards in the course of nicotine addiction. Our results indicate that in contrast to similar studies conducted in alcohol, cannabis and heroin users, the AAT might only be partially suited for measuring smoking-related approach tendencies in smokers. Nevertheless, our findings are of special importance for current etiological models and smoking cessation programs aimed at modifying nicotine-related approach tendencies in the context of a nicotine addiction.

Cotinine is the primary metabolite of nicotine, and a biomarker of tobacco use. It may provide a more precise measure of heaviness of smoking in genetic studies. This study is a genome wide association study (GWAS) meta-analysis of cotinine levels in 4,548 daily smokers of European ancestry.

The global smoking prevalence in 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 North America and Caribbean.

This table provides data from 2021 on the estimated population aged 16 and over who smokes daily in the Canary Islands by number of cigarettes consumed per day, sexes and age groups.

This data set is part of the Nijmegen Bladder Cancer Study, one of the largest series of bladder cancer in the world (see https://icbc.cancer.gov/). The data were used to investigate the relationship between smoking and bladder cancer aggressiveness at diagnosis. The results will be published as Barbosa A.L.A. et al., Smoking intensity and bladder cancer aggressiveness at diagnosis. Plos One (submitted).The Nijmegen Bladder Cancer Study (NBCS) has been described in more detail in (http://www.ncbi.nlm.nih.gov/pubmed/25023787). Briefly, BC patients diagnosed between 1995-2011 under the age of 75 years in the mid-eastern part of the Netherlands were identified through the Netherlands Cancer Registry (NCR) held by the Netherlands Comprehensive Cancer Organization (IKNL) and contacted via their treating physicians. Patients who consented to participate in the study were asked to fill out a lifestyle questionnaire, including questions on education, occupation, medical history, physical activity, and complete history of smoking. Furthermore, blood samples were collected by Thrombosis Service centers, which hold offices in all the communities in the region. The study was approved by the institutional review board of the Radboud university medical center, Nijmegen, The Netherlands (CMO Arnhem-Nijmegen). A total of 1859 BC patients were included in the study.Smoking assessmentInformation on smoking history was obtained via the lifestyle questionnaire. Patients were asked for their smoking status at recruitment, age at smoking initiation and cessation, number of cigarettes, pipes and cigars smoked per day and duration of smoking in years. The timing of smoking cessation with respect to the diagnosis was calculated as age at diagnosis minus age at cessation. Smoking status at diagnosis was classified as never smoker, former smoker (quitted >1 year before diagnosis), current smoker (continuing cigarette smoker or quitted ≤ 1 year before diagnosis). Ever smokers were defined as the combination of former and current smokers. In the current smokers group, only the smoking period in years before the diagnosis was considered. Smoking amount was evaluated as cigarettes per day. Cumulative smoking exposure (in pack-years) was calculated by multiplying the cigarette smoking duration and packages per day (20 cigarettes representing one package). Pipe and/or cigar smoking (5.9% of all patients) was ignored in the main analyses, assuming that the majority of Dutch pipe and cigar smokers do not inhale the smoke.Outcome assessmentDetailed clinical data concerning age at diagnosis, tumor stage, tumor grade, tumor number (single or multiple), tumor size (<3cm and ≥ 3cm), presence of concomitant CIS, and histological type were collected through a medical file survey. Tumor stage and grade were recorded according to the final conclusion in the pathology report. Tumors with WHO 1973 differentiation grade 1 or 2, WHO/ISUP 2004 low grade, or Malmström (Modified Bergkvist) grade 1 or 2a were considered low-grade tumors. We classified tumors with WHO 1973 differentiation grade 3, WHO/ISUP 2004 high grade, or Malmström (Modified Bergkvist) grade 2b or 3 as high-grade. Tumor aggressiveness was classified according to the risk of progression as follows: low-risk NMIBC (low-grade Ta tumors), high-risk NMIBC (all stage T1 tumors, all high-grade tumors, or CIS) and MIBC (stage ≥ T2 or any stage with ≥N1 and/or M1 ).Statistical analysisPatient and tumor characteristics were compared between the smoking status categories using chi-square, Fisher exact, and one-way analysis of variance (ANOVA) tests where appropriate. The distribution of continuous smoking variables was compared between the categories of tumor multiplicity and tumor aggressiveness and tested for statistical significance using the non-parametric Kruskal-Wallis test. Multinomial logistic regression was used to analyze the relation between smoking intensity and aggressiveness of the tumor with adjustment for gender and age at diagnosis. Low-risk NMIBC was considered as the reference group. We repeated similar analyses for tumor multiplicity as the dependent variable using solitary tumors as the reference group. The association of each smoking intensity variable (smoking amount, smoking duration and cumulative smoking exposure), age at smoking initiation, and time since smoking cessation was assessed separately in ever, former and current smokers. Statistical analysis was performed using IBM SPSS Statistics for Windows 20 (IBCM Corp., Armonk, NY, USA) with a p value < 0.05 indicating statistical significance.This dataset contains the statistical datafile (SPSS) used for the data analyses, saved as a .sav and a .por.