This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

As of 2023, the U.S. states with the highest smoking rates included West Virginia, Tennessee, and Louisiana. In West Virginia, around 20 percent of all adults smoked as of this time. The number of smokers in the United States has decreased over the past decades. Who smokes? The smoking rates for both men and women have decreased for many years, but men continue to smoke at higher rates than women. As of 2021, around 13 percent of men were smokers compared to 10 percent of women. Concerning race and ethnicity, smoking is least prevalent among Asians with just five percent of this population smoking compared to 13 percent of non-Hispanic whites. Health impacts of smoking The negative health impacts of smoking are vast. Smoking increases the risk of heart disease, stroke, and many different types of cancers. For example, smoking is estimated to be attributable to 81 percent of all deaths from lung cancer among adults 30 years and older in the United States. Smoking is currently the leading cause of preventable death in the United States.

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.

As of 2021, around 28.3 million adults in the United States were current cigarette smokers. Although this figure is still high, it is significantly lower compared to previous years. For example, in 2011, there were almost 44 million smokers in the United States.

Smoking demographics in the U.S. Although smoking in the U.S. has decreased greatly over the past few decades it is still more common among certain demographics than others. For example, men are more likely to be current cigarette smokers than women, with 13 percent of men smoking in 2021, compared to 10 percent of women. Furthermore, non-Hispanic whites and non-Hispanic Blacks smoke at higher rates than Hispanics and non-Hispanic Asians, with almost 13 percent of non-Hispanic whites smoking in 2021, compared to just over five percent of non-Hispanic Asians. Certain regions and states also have a higher prevalence of smoking than others, with around 20 percent of adults in West Virginia considered current smokers, compared to just six percent in Utah.

The health impacts of smoking The decrease in smoking rates in the United States over the past decades is due to many factors including policies and regulations limiting cigarette advertising, promotion, and sales, price increases for cigarettes, and widespread awareness among the public of the dangers of smoking. According to the CDC, those who smoke are two to four times more likely to develop coronary heart disease and stroke and around 25 times more likely to develop lung cancer than nonsmokers. In fact, it is estimated that around 81 percent of lung cancer deaths in the United States can be attributed to cigarette smoking, as well as 72 percent of larynx cancer deaths. Cigarette smokers are also much more likely to develop chronic obstructive pulmonary disease (COPD), with around 16 percent of current smokers in the U.S. living with COPD in 2021, compared to just three percent of those who had never smoked.

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).

This data shows the percentage of adults (age 18 and over) who are current smokers. Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs. Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities. This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). The percentage of adults is not age-standardised. In this dataset particularly at district level there may be inherent statistical uncertainty in some data values. Thus as with many other datasets, this data should be used together with other data and resources to obtain a fuller picture. Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 92443 (Number 15). This data is updated annually.

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

Study

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

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

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

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

Data

This dataset contains three types of data:

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

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

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

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

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

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

This data shows the percentage of adults (age 18 and over) who are current smokers.

Smoking is the single biggest cause of preventable death and illnesses, and big inequalities exist between and within communities. Smoking is a major risk factor for many diseases, such as lung cancer, chronic obstructive pulmonary disease (COPD, bronchitis and emphysema) and heart disease. It is also associated with cancers in other organs.

Smoking is a modifiable lifestyle risk factor. Preventing people from starting smoking is important in reducing the health harms and inequalities.

This data is based on the Office for National Statistics (ONS) Annual Population Survey (APS). 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.

From 1965 to 2019, the prevalence of cigarette smoking in the U.S. has decreased from about 42 percent to 14 percent. Cigarette smoking is a known risk factor for many types of cancers including lung cancer, bladder cancer and pancreatic cancer. Globally tobacco use is one of the greatest risk factors for preventable diseases. There are several resources in the United States to help individuals quit smoking including website, hotlines, medications and text message programs.

Smoking prevalence globally

Globally, smoking prevalence has also decreased is projected to continue to decline through 2025. North America makes up a small percentage of the world’s cigarette smokers. The highest prevalence of tobacco smoking can be found in Europe, followed by the Western Pacific. In the past few decades there have been stronger efforts made to reduce cigarette consumption in many parts of the world. Cigarettes are taxed separately in many countries and are often required to add health warnings to cigarette packaging for consumers.

Smoking cessation measures

Smoking prevention measures cover a broad range of targeted cigarette reduction. Common tobacco control policies include warning labels, advertising bans, and smoke-free environments. As of 2020, around 60 percent of the world population lived in a place where there were warning labels on tobacco products. Furthermore, in 2020, around 34 percent of U.S. employers offered smoking cessation programs to their employees.

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).

Number of Deaths Attributable to Smoking per 100,000 population by borough.

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.

Numbers of adults smoking by borough.

- 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

This table presents a wide variety of historical data in the field of health, lifestyle and health care. Figures on births and mortality, causes of death and the occurrence of certain infectious diseases are available from 1900, other series from later dates. In addition to self-perceived health, the table contains figures on infectious diseases, hospitalisations per diagnosis, life expectancy, lifestyle factors such as smoking, alcohol consumption and obesity, and causes of death. The table also gives information on several aspects of health care, such as the number of practising professionals, the number of available hospital beds, nursing day averages and the expenditures on care. Many subjects are also covered in more detail by data in other tables, although sometimes with a shorter history. Data on notifiable infectious diseases and HIV/AIDS are not included in other tables.

Data available from: 1900

Status of the figures:

2024: The available figures are definite. 2023: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare; - perinatal and infant mortality. 2022: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - expenditures on health and welfare. 2021: Most available figures are definite. Figures are provisional for: - occurrence of infectious diseases; - expenditures on health and welfare. 2020 and earlier: Most available figures are definite. Due to 'dynamic' registrations, figures for notifiable infectious diseases, HIV, AIDS remain provisional.

Changes as of 18 december 2024: - Due to a revision of the statistics Health and welfare expenditure 2021, figures for expenditure on health and welfare have been replaced from 2021 onwards. - Revised figures on the volume index of healthcare costs are not yet available, these figures have been deleted from 2021 onwards.

The most recent available figures have been added for: - live born children, deaths; - occurrence of infectious diseases; - number of hospital beds; - expenditures on health and welfare; - perinatal and infant mortality; - healthy life expectancy; - causes of death.

When will new figures be published? July 2025.

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

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who use or do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete an interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population 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. 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 the Wave 7 adult and youth respondents from the Wave 4 Cohorts who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0002 (DS0002) contains the data from the State Design Data. This file contains 7 variables and 82,139 cases. The state identifier in the State Design file reflects the participant's state of residence at the time of selection and recruitment for the PATH Study. Dataset 1011 (DS1011) contains the data from the Wave 1 Adult Questionnaire. This data file contains 2,021 variables and 32,320 cases. Each of the cases represents a single, completed interview. Dataset 1012 (DS1012) contains the data from the Wave 1 Youth and Parent Questionnaire. This file contains 1,431 variables and 13,651 cases. Dataset 1411 (DS1411) contains the Wave 1 State Identifier data for Adults and has 5 variables and 32,320 cases. Dataset 1412 (DS1412) contains the Wave 1 State Identifier data for Youth (and Parents) and has 5 variables and 13,651 cases. The same 5 variables are in each State Identifier dataset, including PERSONID for linking the State Identifier to the questionnaire and biomarker data and 3 variables designating the state (state Federal Information Processing System (FIPS), state abbreviation, and full name of the state). The State Identifier values in these datasets represent participants' state of residence at the time of Wave 1, which is also their state of residence at the time of recruitment. Dataset 1611 (DS1611) contains the Tobacco Universal Product Code (UPC) data from Wave 1. This data file contains 32 variables and 8,601 cases. This file contains UPC values on the packages of tobacco products used or in the possession of adult respondents at the time of Wave 1. The UPC values can be used to identify and validate the specific products used by respon

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;

It is projected that the prevalence of tobacco use among those aged 15-24 years will decrease from 20.5 percent in 2000 to 11.8 percent in 2030. This statistic depicts the prevalence of tobacco use worldwide from 2000 to 2022 and projections for 2025 and 2030, by age

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.

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.

Analysis of ‘Stroke Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/redwan1010/stroke-data on 28 January 2022.

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

Context

According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relevant information about the patient.

Content

1) id: unique identifier 2) gender: "Male", "Female" or "Other" 3) age: age of the patient 4) hypertension: 0 if the patient doesn't have hypertension, 1 if the patient has hypertension 5) heart_disease: 0 if the patient doesn't have any heart diseases, 1 if the patient has a heart disease 6) ever_married: "No" or "Yes" 7) work_type: "children", "Govt_jov", "Never_worked", "Private" or "Self-employed" 8) Residence_type: "Rural" or "Urban" 9) avg_glucose_level: average glucose level in blood 10) bmi: body mass index 11) smoking_status: "formerly smoked", "never smoked", "smokes" or "Unknown"* 12) stroke: 1 if the patient had a stroke or 0 if not *Note: "Unknown" in smoking_status means that the information is unavailable for this patient

Acknowledgements

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Inspiration

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