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.

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 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.

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.

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

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. For the 1998 survey, the coverage of smoking issues was further reduced, in order to include a number of questions about drug use. Questions about the smoking behaviour of family members were dropped from the English survey in order to accommodate these, but were still asked in Scotland, where the drug use questions were restricted to actual drug use only. The questions dropped from the English survey were, however, included in the corresponding HEA Teenage Smoking Attitudes Survey, 1998 (held at the Archive under SN 4120). A new Scottish file with a number of extra schedule variables and derived variables was deposited in October 2000 to replace the original. Please see read file for further details. Main Topics: There are two datasets for this study, one for England and one for Scotland. The datasets include variables from the questionnaire, diary and cotinine analysis. Topics covered in the questionnaire include: demographic details, smoking behaviour, the purchase of cigarettes in shops and other outlets, parental attitudes to smoking, family smoking behaviour (asked in Scotland only), alcohol use and consumption in previous week, actual drug use (both surveys), attitudes to drug use (asked in England only), and health education lessons at school. For the diary, pupils were asked to record by retrospective recall all cigarettes smoked in the previous seven days. Multi-stage stratified random sample Self-completion Clinical measurements

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.

Tobacco smoking is one of the largest preventable causes of death and disease in Australia. In 2017-18, 13.8% of adults aged 18 years and over were daily smokers (2.6 million people), down from 14.5% in 2014-15. The decrease is a continuation of the trend over the past two decades, in 1995, 23.8% of adults were daily smokers.

Additionally the proportion of adults who have never smoked is increasing over time, from 49.4% in 2007-08 to 52.6% in 2014-15 and 55.7% in 2017-18.

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.

This report presents statistics on women’s smoking status at time of delivery, at Sub Integrated Care Board (Sub-ICB), Integrated Care Board (ICB), regional and national levels. This release includes provisional data for quarter 3 of 2024-25 using data from the Smoking at Time of Delivery data collection which is submitted by commissioners (presented as SATOD v1). Alongside this and for the third time, comparative data using the Maternity Services Dataset (MSDS) is also presented using data submitted by Trusts (presented as SATOD v2) as a time series from quarter 1 of 2022-23 to quarter 3 of 2024-25. This is available for the same geographical breakdowns and includes an additional breakdown for Local Authorities. This will be repeated for subsequent quarters in 2024-25 to see how the estimates from both data sources align with a view to retiring the Smoking at Time of Delivery data collection at the end of this financial year. Until then, SATOD v1 remains the primary data source for this publication. In 2024, a proposal for the data source for this publication to be changed to the Maternity Services Dataset was included in a wider consultation: Health and social care statistical outputs published by DHSC (including OHID), NHSBSA, UKHSA, ONS and NHS England. A link to this is in the Related Links below. If you would still like to feedback your views on the SATOD data collection retirement and replacement with MSDS, then please contact us on: england.maternityanalysis@nhs.net

Adolescents Who Use Tobacco Products - This indicator shows the percentage of adolescents (public high school students) who used any tobacco product in the last 30 days. Preventing youth from using tobacco products is critical to improving the health of Marylanders. This highly addictive behavior can lead to costly illnesses and death to users and those exposed to secondhand smoke. Link to Data Details

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. Ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

This is the analysis code underlying the paper "Attitudes Toward a Virtual Smoking Cessation Coach: Relationship and Willingness to Continue" by Nele Albers, Mark A. Neerincx, Nadyne L. Aretz, Mahira Ali, Arsen Ekinci and Willem-Paul Brinkman. In this paper, we conduct a mixed-methods analysis of people's relationship and willingness to continue working with the text-based virtual coach Sam.

Data:

Our analysis is based on the data collected in an online experiment in which more than 500 daily smokers interacted with the text-based virtual coach (i.e., a conversational agent) in up to 5 sessions spread over at least 9 days. In each session, Sam proposed a new preparatory activity for quitting smoking or becoming more physically active, with the latter possibly aiding the former. After the 5 sessions, participants filled in a post-questionnaire in which they answered 6 questions about their attitude toward Sam by means of a rating on a scale from -5 to 5 and a free-text response to the follow-up question "Why do you think so?" The questions were adapted based on the ones by Provoost et al. (2020). Our paper focuses on people's responses to the questions "Do you prefer continuing or stopping to work with the conversational agent Sam?" (-5: "Definitely prefer stopping", 5: "Definitely prefer continuing") and "How would you characterize your relationship with the conversational agent Sam?" (-5: "Complete stranger", 5: "Close friend"). The complete dataset can be found here: https://doi.org/10.4121/19934783.v1. Notably, this dataset contains only data used to study the acceptance of the virtual coach Sam. Further data from the same study has been published in separate repositories (e.g., https://doi.org/10.4121/20284131.v2).

Virtual coach:

The implementation of the virtual coach is available here: https://doi.org/10.5281/zenodo.6319356.

Analysis:

Our mixed-methods analysis is based on the publicly available Bachelor's theses by Nadyne L. Aretz and Mahira Ali:

• Reasons for wanting to continue or stop using a virtual coach for quitting smoking and increasing physical activity: http://resolver.tudelft.nl/uuid:cb4e9c2e-27fb-4074-95f0-307d4d964be9.
• Relationship with Sam: http://resolver.tudelft.nl/uuid:a79c34d7-ab6f-4385-b566-c0c3302a580e.

In this repository, we provide all code needed to reproduce the analyses reported in our paper. We thereby largely rely on Docker. Please refer to the README-file for more information.

In case of questions about the analysis or data, please contact Nele Albers (n.albers@tudelft.nl).

This dataset presents the footprint of the age-standardised percentage of adults who are daily smokers. A current daily smoker was defined as a person who smokes one or more cigarettes, roll-your-own cigarettes, cigars or pipes at least once a day. Chewing tobacco, electronic cigarettes (and similar) and the smoking of non-tobacco products were excluded. As an indication of the accuracy of estimates, 95% confidence intervals were produced. These were calculated by the Australian Bureau of Statistics (ABS) using standard error estimates of the proportion. The data spans the financial year of 2014-2015 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Health risk factors are attributes, characteristics or exposures that increase the likelihood of a person developing a disease or health disorder. Examples of health risk factors include risky alcohol consumption, physical inactivity and high blood pressure. High-quality information on health risk factors is important in providing an evidence base to inform health policy, program and service delivery. For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Health Risk Factors in 2014-2015 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Age-standardisation is a method of removing the influence of age when comparing populations with different age structures - either different populations at the same time or the same population at different times. For this data the Australian estimated resident population of people aged 18 and over as at 30 June 2001 has been used as the standard population. Adults are defined as persons aged 18 years and over. Values assigned to "n.p." in the original data have been removed from the data.

In 2018, there were more than 371 million cigarette smokers and 12. 6 million electronic cigarette users, with 340.2 million non-smokers exposed to secondhand smoke (SHS) in China, which resulted in heavy tobacco-attributable disease burden. According to the definition by the Global Burden of Disease Study 2017 (GBD 2017), tobacco is a level 2 risk factor that consists of three sublevel risk factors, namely, smoking, SHS, and chewing tobacco. In this study, we aimed to evaluate the trends in deaths and disability-adjusted life years (DALYs) attributable to tobacco, smoking, SHS, and chewing tobacco by sex in China from 1990 to 2017 and to explore the leading causes of tobacco-attributable deaths and DALYs using data from the GBD 2017. From 1990 to 2017, the tobacco-attributable death rates per 100,000 people decreased from 75.65 [95% uncertainty interval (95% UI) = 56.23–97.74] to 70.90 (95% UI = 59.67–83.72) in females and increased from 198.83 (95% UI = 181.39–217.47) to 292.39 (95% UI = 271.28–313.76) in males. From 1990 to 2017, the tobacco-attributable DALY rates decreased from 2209.11 (95% UI = 1678.63–2791.91) to 1489.05 (95% UI = 1237.65–1752.57) in females and increased from 5650.42 (95% UI = 5070.06–6264.39) to 6994.02 (95% UI = 6489.84–7558.41) in males. In 2017, the tobacco-attributable deaths in China were concentrated on chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, and stroke. The focus of tobacco control for females was SHS in 1990, whereas smoking and SHS were equally important for tobacco control in females in 2017. Increasing tobacco taxes and prices may be the most effective and feasible measure to reduce tobacco-attributable disease burdens.

Data set of annual questionnaires of a long-term prospective study of 1,337 former Johns Hopkins University medical students to identify precursors of premature cardiovascular disease and hypertension. The purpose of the study has broadened, however, as the cohort has aged. The study has been funded for 15 years. Participants were an average of 22 years of age at entry and have been followed to an average age of 69 years. Data are collected through annual questionnaires, supplemented with phone calls and substudies. Self-reports of diseases and risk factors have been validated. Every year from 1988 to 2003, anywhere from 2 to 6 questionnaires have been administered, in categories such as the following, which repeat periodically: Morbidity, Supplemental Illness, Health Behavior, Family and Career, Retirement, Job Satisfaction, Blood Pressure and Weight, Medications, Work Environment, Social Network, Diabetes, Osteoarthritis, Health Locus of Control, Preventive Health Services, General Health, Functional Limitations, Memory Functioning, Smoking, Religious Beliefs and Practices, Links with Administrative Data, National Death Index searches for all nonrespondents * Dates of Study: 1946-2003 * Study Features: Longitudinal * Sample Size: 1,337 (1946)

IntroductionPromoting smoking cessation is a global public health priority. E-cigarettes are increasingly being used by individuals to try quitting smoking. Identifying sources and types of information available to adults who are trying to quit, and the impact of this information during a quit attempt, is critical to augment the potential public health benefit of e-cigarettes for reducing cigarette smoking.MethodsUS adults (N = 857) who reported using e-cigarettes in a recent smoking cessation attempt completed an anonymous, cross sectional, online survey. We examined sources of information and type of information received when using e-cigarettes to quit smoking and their associations with the duration of abstinence achieved.ResultsThe two most commonly reported information sources were friends (43.9%) and the internet (35.2%), while 14.0% received information from a healthcare provider. People received information on type of device (48.5%), flavor (46.3%), and nicotine concentration (43.6%). More people received information about gradually switching from smoking to vaping (46.7%) than abruptly switching (30.2%). Obtaining information from healthcare providers (β (SE) = 0.16 (0.08), p = 0.04), getting information about abruptly switching to e-cigarettes (β (SE) = 0.14 (0.06), p = 0.01) and what nicotine concentrations to use (β (SE) = 0.18 (0.05), p = 0.03) were associated with longer quit durations.ConclusionsAmidst the growing popularity of e-cigarettes use for quitting smoking, our results highlight common sources of information and types of information received by individuals. Few people received information from healthcare providers indicating a gap in cessation support that can be filled. Providing information about immediate switching to e-cigarettes and nicotine concentrations to use may help in increasing quit rates and duration.

BackgroundMental health service users are more likely to smoke tobacco and are as likely to make quit attempts as people not experiencing SMI, but they are less likely to succeed. Quitting tobacco can be harder for people experiencing SMI due to higher levels of nicotine dependence, more severe withdrawal, and many other complex factors. The Quitlink study was a randomized controlled trial combining a tailored 8-week Quitline intervention delivered by dedicated Quitline counsellors plus combination nicotine replacement therapy for people who experience SMI. The purpose of this paper is to report on the medium- and longer-term findings from interviews conducted at 5 and 8 months.MethodsAs a part of the broader Quitlink study, participants were invited to qualitative interviews at 2, 5 and 8 months following recruitment, in line with quantitative follow-up time points. Interviews were conducted with 28 participants in the Quitlink trial (intervention group n = 12, control group n = 16). Interviews were transcribed and analyzed with a thematic analysis methodology using NVivo 12. Key themes were determined using inductive coding.ResultsSix key themes were identified. These included: internal/external attributions for tobacco smoking, social relationships and relapse, the role of hopefulness in quitting, the role of clinicians in initiating and maintaining a quit attempt, increasing cessation literacy, and efficacy of the study intervention. Overall, findings suggested that participants’ quit attempts were often precarious and vulnerable, but active support and feelings of social connectedness were key to supporting participants to initiate a quit attempt and maintain gains.ConclusionsPeople who experience SMI can make attempts to quit smoking tobacco with support from clinicians and social networks. Connectedness and hope are significant enablers of making and sustaining quit attempts.Trial registrationThe Quitlink trial was registered with ANZCTR (www.anzctr.org.au): ACTRN12619000244101 prior to the accrual of the first participant and updated regularly as per registry guidelines.

This chart shows the percentage of homes with at least one smoker at the initial visit and revisit, by county for the 2009-2014 funding cycle. The chart gives an indication of the ability of the HNP’s smoking intervention (referrals and education) to decrease the prevalence of smoking in the home. The initial visit percentages range across counties from about 8% to almost 60%. There was a substantial reduction in Albany County, but most counties are essentially unchanged which may indicate the difficulty in changing people’s smoking habits. Three counties show no homes with smokers at the revisit, but these are newly funded counties that have conducted very few revisits to date. Because revisits are a subset of the initial visits, closer examination of the dataset is necessary to confirm the number of initial visits and revisits that were conducted before drawing conclusions. Please read the overview document under the “About” tab for more information on the limitations.

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program