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.

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.

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).

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

This report presents newly published information on smoking including: Smoking-related hospital admissions from NHS Digital's Hospital Episode Statistics (HES). Smoking-related deaths from Office for National Statistics (ONS) mortality statistics. Prescription items used to help people stop smoking from prescribing data held by NHS Prescription Services. Affordability of tobacco and expenditure on tobacco using ONS economic data. Two new years of data have been provided for hospital admissions (2018/19 and 2019/20) and deaths (2018 and 2019) and one year of data for prescribing (2018/19) and affordability and expenditure (2019). The report also provides links to information on smoking by adults and children drawn together from a variety of sources. Key facts cover the latest year of data available: Hospital admissions: 2019/20 Deaths: 2019 Prescriptions: 2019/20

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: 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 do and 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 Units (PSUs) 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 Public-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. Wave 4.5 was a special data collection for youth only who were aged 12 to 17 at the time of the Wave 4.5 interview. Wave 4.5 was the fourth annual follow-up wave for those who were members of the Wave 1 Cohort. For those who were sampled at Wave 4, Wave 4.5 was the first annual follow-up wave.Wave 5.5, conducted in 2020, was a special data collection for Wave 4 Cohort youth and young adults ages 13 to 19 at the time of the Wave 5.5 interview. Also in 2020, a subsample of Wave 4 Cohort adults ages 20 and older were interviewed via the PATH Study Adult Telephone Survey (PATH-ATS).Wave 7.5 was a special collection for Wave 4 and Wave 7 Cohort youth and young adults ages 12 to 22 at the time of the Wave 7.5 interview. For those who were sampled at Wave 7, Wave 7.5 was the first annual follow-up wave. Dataset 1002 (DS1002) contains the data from the Wave 4.5 Youth and Parent Questionnaire. This file contains 1,395 variables and 13,131 cases. Of these cases, 11,378 are continuing youth having completed a prior Youth Interview. The other 1,753 cases are "aged-up youth" having previously been sampled as "shadow youth." Datasets 1112, 1212, and 1222, (DS1112, DS1212, and DS1222) are data files comprising the weight variables for Wave 4.5. The "all-waves" weight file contains weights for participants in the Wave 1 Cohort who completed a Wave 4.5 Youth Interview and completed interviews (if old enough to do so) or verified their information with the study (if not old enough to be interviewed) in Waves 1, 2, 3, and 4. There are two separate files with "single wave" weights: one for the Wave 1 Cohort and one for the Wave 4 Cohort. The "single-wave" weight file for the Wave 1 Cohort contains weights for youth who completed an interview in Wave 1 an

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.

This dataset contains statistical data on the percentage of people affected by secondhand smoking, categorized by gender and type of exposure.

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). 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 2.14. This data is updated annually.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

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.

Analysis of ‘Healthy People 2020 Tobacco Use Objectives’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/98fa8403-74e0-444c-b71d-7528fdf59d34 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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

The Population Assessment of Tobacco and Health (PATH) Study began originally surveying 45,971 adult and youth respondents. The PATH Study was launched in 2011 to inform 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 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 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 civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort.Dataset 0001 (DS0001) contains the data from the Master Linkage file. This file contains 14 variables and 67,276 cases. The file provides a master list of every person's unique identification number and what type of respondent they were for each wave. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population 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 Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population 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 Public-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 1001 (DS1001) contains the data from the Wave 1 Adult Questionnaire. This data file contains 1,732 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1002 (DS1002) contains the data from the Youth and Parent Questionnaire. This file contains 1,228 variables and 13,651 cases.Dataset 2001 (DS2001) contains the data from the Wave 2 Adult Questionnaire. This data file contains 2,197 variables and 28,362 cases. Of these cases, 26,447 also completed a Wave 1 Adult Questionnaire. The other 1,915 cases are "aged-up adults" having previously completed a Wave 1 Youth Questionnaire. Dataset 2002 (DS2002) contains the data from the Wave 2 Youth and Parent Questionnaire. This data file contains 1,389 variables and 12,172 cases. Of these cases, 10,081 also completed a Wave 1 Youth Questionnaire. The other 2,091 cases are "aged-up youth" having previously been sampled as "shadow youth." Dataset 3001 (DS3001) contains the data from the Wave 3 Adult Questionnaire. This data file contains 2,139 variables and 28,148 cases. Of these cases, 26,241 are continuing adults having completed a prior Adult Questionnaire. The other 1,907 cases are "aged-up adults" having previously completed a Youth Questionnaire. Dataset 3002 (DS3002) contains the data from t

Smoking Person Detection

## Overview

Smoking Person Detection is a dataset for object detection tasks - it contains Smoke annotations for 977 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This repository contains 523 human feedback messages sent to daily smokers and vapers who were preparing to quit smoking/vaping with a virtual coach.

Study

Daily smokers and vapers recruited through the online crowdsourcing platform Prolific interacted with the text-based virtual coach Kai in up to five sessions between 1 February and 19 March 2024. The sessions were 3-5 days apart. In each session, participants were assigned a new preparatory activity for quitting smoking (e.g., listing reasons for quitting smoking, envisioning one's desired future self after quitting smoking, doing a breathing exercise). Between sessions, participants had a 20% chance of receiving a feedback message from one of two human coaches. More information on the study can be found in the Open Science Framework (OSF) pre-registration: https://doi.org/10.17605/OSF.IO/78CNR. The implementation of the virtual coach Kai can be found here: https://doi.org/10.5281/zenodo.11102861.

Feedback messages

All feedback messages were written by one of two Master's students in psychology. The two human coaches were directed to craft messages incorporating feedback, argument, and either a suggestion or reinforcement. They were also instructed to connect with individuals by referencing aspects of their lives, express empathy toward those with low confidence, and provide reinforcement when people were motivated.

When writing the feedback, the human coaches had access to data on people's baseline smoking and physical activity behavior (i.e., smoking/vaping frequency, weekly exercise amount, existence of previous quit attempts of at least 24 hours, and the number of such quit attempts in the last year), introduction texts from the first session with the virtual coach, previous preparatory activity (i.e., activity formulation, effort spent on the activity and experience with it, return likelihood), current state (i.e., self-efficacy, perceived importance of preparing for quitting, human feedback appreciation), and new activity formulation. Notably, the human coaches only had access to anonymized versions of the introduction texts and activity experience responses (e.g., names were removed). Except for the free-text responses describing participants' experiences with their previous activity and their introduction texts, all of this information is provided together with the feedback messages. For the previous and new activities, we just provide the titles and not also the entire formulations that the human coaches had access to.

Before sending the messages to participants on Prolific, we added a greeting (i.e., "Best wishes, Karina & Goda on behalf of the Perfect Fit Smoking Cessation Team"), a disclaimer that the messages were not medical advice, and a link to confirm having read the message at the end. We also added "This is your feedback message from your human coaches Karina and Goda for preparing to quit [smoking/vaping]:" at the start of the message.

The human coaches approved publishing these feedback messages.

Additional data from the study

Additional data from the study such as participants' free-text descriptions of their experiences with their activities and their introductions from the first session with the virtual coach will also be published and linked to the OSF pre-registration of the study.

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

Dataset

This dataset was created by Han Lee

Released under CC BY-NC-SA 4.0

Contents

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