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 global number of smokers in was forecast to continuously increase between 2024 and 2029 by in total 13.9 million individuals (+1.29 percent). After the eleventh consecutive increasing year, the number of smokers is estimated to reach 1.1 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 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 number of smokers in countries like Caribbean and Africa.

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

This report contains results from the latest survey of secondary school pupils in England in years 7 to 11 (mostly aged 11 to 15), focusing on smoking, drinking and drug use. It covers a range of topics including prevalence, habits, attitudes, and wellbeing. This survey is usually run every two years, however, due to the impact that the Covid pandemic had on school opening and attendance, it was not possible to run the survey as initially planned in 2020; instead it was delivered in the 2021 school year. In 2021 additional questions were also included relating to the impact of Covid. They covered how pupil's took part in school learning in the last school year (September 2020 to July 2021), and how often pupil's met other people outside of school and home. Results of analysis covering these questions have been presented within parts of the report and associated data tables. It includes this summary report showing key findings, excel tables with more detailed outcomes, technical appendices and a data quality statement. An anonymised record level file of the underlying data on which users can carry out their own analysis will be made available via the UK Data Service later in 2022 (see link below).

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

Comparing the 126 selected regions regarding the smoking prevalence , Myanmar is leading the ranking (42.49 percent) and is followed by Serbia with 39.33 percent. At the other end of the spectrum is Ghana with 3.14 percent, indicating a difference of 39.35 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 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).

The global smoking prevalence in was forecast to continuously decrease between 2024 and 2029 by in total 1.5 percentage points. After the eighth consecutive decreasing year, the smoking prevalence is estimated to reach 20.66 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 North America and Caribbean.

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%.

Abstract copyright UK Data Service and data collection copyright owner.

The Smoking, Drinking and Drug Use among Young People surveys began in 1982, under the name Smoking among Secondary Schoolchildren. The series initially aimed to provide national estimates of the proportion of secondary schoolchildren aged 11-15 who smoked, and to describe their smoking behaviour. Similar surveys were carried out every two years until 1998 to monitor trends in the prevalence of cigarette smoking. The survey then moved to an annual cycle, and questions on alcohol consumption and drug use were included. The name of the series changed to Smoking, Drinking and Drug Use among Young Teenagers to reflect this widened focus. In 2000, the series title changed, to Smoking, Drinking and Drug Use among Young People. NHS Digital (formerly the Information Centre for Health and Social Care) took over from the Department of Health as sponsors and publishers of the survey series from 2005. From 2014 onwards, the series changed to a biennial one, with no survey taking place in 2015, 2017 or 2019.

In some years, the surveys have been carried out in Scotland and Wales as well as England, to provide separate national estimates for these countries. In 2002, following a review of Scotland's future information needs in relation to drug misuse among schoolchildren, a separate Scottish series, Scottish Schools Adolescent Lifestyle and Substance Use Survey (SALSUS) was established by the Scottish Executive.

The survey uses a two-stage probability sample of schools and pupils, designed to be representative of young people aged between 11 and 15. The sample of schools is stratified by sex of intake and school type. Within these strata, the sampling frame is sorted by local authority. This design does not guarantee a representative sample of schools within all regions and so reliable estimates by region cannot currently be derived from any one year’s data.

This dataset contains regional information as well as key survey variables from the three most recent survey years, 2006 to 2008, combined and weighted to be regionally representative.

Main Topics:

The dataset includes core responses from all pupils who completed a questionnaire in survey years 2006 to 2008. Broad topics included:

• smoking
• drinking
• drug use
• attitudes to smoking, drinking and drug use
• education
• truancy and exclusion
• background information

This dataset contains action plans for doing preparatory activities (n = 469) and free-text responses to reflective questions about preparatory activities (n = 2026) in the context of quitting smoking and becoming more physically active with a virtual coach. 289 reflections concern the views of experts, 750 the views of similar people, and 987 commitment to one's decision to quit smoking. The dataset also contains data on user characteristics (e.g., age, personality).

Study

The data was gathered during a longitudinal study on the online crowdsourcing platform Prolific between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523).

In this study, 671 smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active can make it easier to quit smoking, half of the activities addressed preparing for becoming more physically active. Sam persuaded people to do their assigned activity using one of five persuasion types (commitment, consensus, authority, action planning, and no persuasion). For the persuasion types of commitment, consensus, and authority, Sam first uttered a persuasive message, followed by a reflective question that participants were asked to provide a free-text response to (e.g. "Please tell me what you think: In what way does doing this activity match your decision to successfully quit smoking?"). For the persuasion type of action planning, participants were asked to type an action plan for doing the activity into the chat. After the five sessions, participants filled in a post-questionnaire in which they were asked about their ease of and motivation to do their preparatory activities via two items each.

The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that this dataset contains only part of the collected data, namely, the data related to studying the reflections and action plans created by participants. Other data from this study has been published in separate datasets:

• Acceptance of the virtual coach: https://doi.org/10.4121/19934783.v1.
• Users' needs for a digital smoking cessation application: https://doi.org/10.4121/20284131.v2.
• Effectiveness of a reinforcement learning-algorithm for persuading to prepare for quitting smoking and becoming more physically active: https://doi.org/10.4121/21533055.v2.

Since the same random participant identifiers are used in these datasets, data from the separate datases can be linked.

Pointers to more information on the study:

• The implementation of the virtual coach Sam can be found here: https://doi.org/10.5281/zenodo.6319356.
• A list of the formulations of the 24 activities used in the study can be found in S8 Appendix of our paper: https://doi.org/10.1371/journal.pone.0277295.
• All reflective questions can be found in Table 3 in the same paper: https://doi.org/10.1371/journal.pone.0277295.
• The structure of the conversations with the virtual coach is visualized in S4 Appendix of the same paper: https://doi.org/10.1371/journal.pone.0277295.

Data

This dataset contains five types of data (explained in the file "_Explanation_of_Data_Files.xlsx"):

• Data from participants' Prolific profiles. This includes data on demographics (e.g., age range, household size, household income) as well as smoking and physical activity behavior (e.g., weekly exercise amount, smoking frequency).
• Data from a pre-screening questionnaire. This includes, for example, the responses to informed consent questions.
• Data from a pre-questionnaire. This includes data on smoking and physical activity behavior, as well as personality and need for cognition.
• Data from the conversational sessions. This includes the action plans and reflective question answers, the effort people spent on their activity from the previous session, people's mood (e.g., "happy", "miserable", "gloomy"), answers to state questions (e.g., having sufficient time to do the assigned activity), and the persuasion type used by the virtual coach.
• Data from a post-questionnaire. This includes data on the ease of and motivation to do the preparatory activities.

There is a separate data file for each type of data. For each data file, there is also a corresponding .xlsx-file explaining each measure in detail.

In case of questions about this dataset, please contact Nele Albers (n.albers@tudelft.nl).

Context

Cannabis Strains

Content

strain name: Given name of strain

type of strain: indica, sativa, hybrid

rating: user ratings averaged

effects: different effects optained

taste: taste of smoke

description: backround, etc

Acknowledgements

leafly.com

Inspiration

Marijuana may get a bad rep in the media as far as the decriminalization debate goes, but its health benefits can no longer go unnoticed. With various studies linking long-term marijuana use to positive, health-related effects, there are more than just a few reasons to smoke some weed every day.

A study done by the Boston Medical Center and the Boston University of Medicine, examined 589 drug users—more than 8 out of 10 of whom were pot smokers. It determined that “weed aficionados” were no more likely to visit the doctor than non-drug users. If an increased risk of contracting ailments is what’s preventing you from smoking more weed, it looks like you’re in the clear!

One of the greatest medicinal benefits of marijuana is its pain relieving qualities, which make it especially effective for treating chronic pain. From menstruation cramps to nerve pain, as little as three puffs of bud a day can help provide the same relief as synthetic painkillers. Marijuana relieves pain by “changing the way the nerves function,” says Mark Ware, MD and assistant professor of anesthesia and family medicine at McGill University.

Studies have found that patients suffering from arthritis could benefit from marijuana use. This is because naturally occurring chemicals in cannabis work to activate pathways in the body that help fight off joint inflammation.

The Hispanic EPESE provides data on risk factors for mortality and morbidity in older Mexican Americans in order to contrast how these factors operate differently than in non-Hispanic Whites, African Americans, and other major ethnic groups.The Wave 9 dataset comprises the eighth follow-up of the baseline Hispanic Established Populations for the Epidemiologic Studies of the Elderly, 1993-1994: Arizona, California, Colorado, New Mexico, and Texas. The baseline Hispanic EPESE collected data on a representative sample of community-dwelling Mexican Americans, aged 65 years and older, residing in the five Southwestern states of Arizona, California, Colorado, New Mexico, and Texas.The public-use data covers demographic characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health behaviors, self-reported use of dental, hospital, and nursing home services, and depression. Subsequent follow-ups allow examination of the predictors of mortality, changes in health outcomes, institutionalization, changes in living arrangements, as well as changes in life situations and quality of life.During this 9th Wave (Dataset 1), 2016, 480 re-interviews were conducted either in person or by proxy, with 283 of the original respondents interviewed in 1993-1994. This Wave also includes 197 re-interviews from the 902 new respondents added at Wave 5 in 2004-2005. All respondents were aged 85 and over at Wave 9.The Wave 9 Informant Interviews dataset (Dataset 2) includes data from interviews with 460 respondents who provided information on themselves as well as the older respondents. The older respondents were asked to provide the name and contact information of the person they are "closer to" or they "depend on the most for help." These INFORMANTS, many of whom provide caregiving support to the older respondents, were contacted, and interviewed regarding the health, function, social situation, finances, and general well-being of the older Hispanic EPESE respondents. Information was also collected on the informant's health, function, and caregiver responsibilities and burden. This dataset includes information from the 460 informants, more than two-thirds of whom were children of the respective respondents. Thus, there are 460 respondent-informant dyads that provide opportunities for caregiving research.

This table contains 90 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years), and was last released on 2010-03-30. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Sex (2 items: Males; Females ...) Age group (3 items: 11 years; 13 years; 15 years ...) Persons (3 items: Mother; Best friend; Father ...) Student response (5 items: Smokes daily; Smokes sometimes; Does not smoke; Don't know ...).

IntroductionLight and intermittent smoking (LITS) has become increasingly common. Alcohol drinkers are more likely to smoke. We examined the association of smokefree law and bar law coverage and alcohol use with current smoking, LITS, and smoking quit attempts among US adults and alcohol drinkers.MethodsCross-sectional analyses among a population-based sample of US adults (n = 27,731) using restricted data from 2009 National Health Interview Survey and 2009 American Nonsmokers' Rights Foundation United States Tobacco Control Database. Multivariate logistic regression models examined the relationship of smokefree law coverage and drinking frequency (1) with current smoking among all adults; (2) with 4 LITS patterns among current smokers; and (3) with smoking quit attempts among 6 smoking subgroups. Same multivariate analyses were conducted but substituted smokefree bar law coverage for smokefree law coverage to investigate the association between smokefree bar laws and the outcomes. Finally we ran the above analyses among alcohol drinkers (n = 16,961) to examine the relationship of smokefree law (and bar law) coverage and binge drinking with the outcomes. All models controlled for demographics and average cigarette price per pack. The interactions of smokefree law (and bar law) coverage and drinking status was examined.ResultsStronger smokefree law (and bar law) coverage was associated with lower odds of current smoking among all adults and among drinkers, and had the same effect across all drinking and binge drinking subgroups. Increased drinking frequency and binge drinking were related to higher odds of current smoking. Smokefree law (and bar law) coverage and drinking status were not associated with any LITS measures or smoking quit attempts.ConclusionsStronger smokefree laws and bar laws are associated with lower smoking rates across all drinking subgroups, which provides further support for these policies. More strict tobacco control measures might help reduce cigarette consumption and increase quit attempts.

The volatile nature of biomass burning organics may complicate the evolution of organics in laboratory smog-chamber experiments and in ambient plumes. We simulate the evolution of organic mass (including gas and particles) in the chamber experiments using the TwO-Moment Aerosol Sectional (TOMAS) microphysics model combined with a secondary organic aerosol (SOA) production matrix. We estimate the effect of vapor wall loss by turning off the vapor wall loss, and also added Gaussian dispersion to our aerosol-microphysical model to SOA formation under different ambient-plume conditions. A detailed description of model setup and results can be found in Bian et al. 2017. The data publication here contains simulation datasets generated using the TOMAS microphysics model combined with a secondary organic aerosol (SOA) production matrix. Datasets are organized according to the figures in Bian et al. 2017 and include 1) chemistry-only simulation data; 2) data generated using the TOMAS model combined with particle and vapor wall-loss algorithms and a SOA production matrix with varying parameters; and 3) simulation data generated using the TOMAS model assuming the plume volume follows the Gaussian dispersion. Each ASCII dataset contains the time series of individual vapors and particles that were distributed in 36 size bins from 3 nanometers to 10 micrometers.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The mass of live and dead vegetation, termed biomass, is used in a variety of applications including carbon mapping, wildland smoke emissions modeling, and fire management. In many mapping projects, single biomass values are often used to represent classified vegetation types. However, in reality, the biomass of grasslands, shrublands and forests is extremely variable, and mapped values are associated with a high degree of uncertainty. In this study, we developed a database of wildland fuel biomass (North American Wildland Fuel Database) by major vegetation type (i.e., tree crowns, snags, shrubs, herb, downed wood by size class, litter and duff) in the United States and Canada. This database is a compilation of existing databases and published literature, with wildland fuel biomass converted to a standard unit of measure (megagrams per hectare).The primary goal of this study is to characterize the inherent variability in wildland fuels and facilitate uncertainty analysis in modeling applications. Published distributions will be useful for informing the first-generation biomass and wildland fuels mapping that incorporates uncertainty estimates by major category. Results of this study also will help inform future sampling needs to better represent the biomass of wildland fuels. Because biomass is a common input in fire and smoke models, uncertainty intervals informed by our database can be used to better understand uncertainty in predictions of wildland fuel consumption and emissions and in regional to national mapping applications for biomass, carbon and emissions inventories.These data were originally published on 06/21/2019. Minor metadata updates were made on 03/04/2024.

Death rate has been age-adjusted by the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Lung cancer is a leading cause of cancer-related death in the US. People who smoke have the greatest risk of lung cancer, though lung cancer can also occur in people who have never smoked. Most cases are due to long-term tobacco smoking or exposure to secondhand tobacco smoke. Cities and communities can take an active role in curbing tobacco use and reducing lung cancer by adopting policies to regulate tobacco retail; reducing exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing; and improving access to tobacco cessation programs and other preventive services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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

🇸🇪 스웨덴