United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data was reported at 21.800 % in 2016. This records a decrease from the previous number of 22.300 % for 2015. United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data is updated yearly, averaging 23.900 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 31.400 % in 2000 and a record low of 21.800 % in 2016. United States US: 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 USA – Table US.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;

Historical chart and dataset showing U.S. smoking rate by year from 2000 to 2022.

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...

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.

This dataset was extracted from the US arm of the International Tobacco Control (ITC) Four Country Smoking and Vaping Survey Wave 2 (2018) and Wave 3 (2020) datasets. This dataset was used to conduct the analysis presented in the publication "Was COVID-19 associated with increased cigarette purchasing, consumption, and smoking at home among US smokers in early 2020? Findings from the US arm of the International Tobacco Control Four Country Smoking and Vaping Survey."

Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].

The 2011 BRFSS data reflects a change in weighting methodology (raking) and the addition of cell phone only respondents. Shifts in observed prevalence from 2010 to 2011 for BRFSS measures will likely reflect the new methods of measuring risk factors, rather than true trends in risk-factor prevalence. A break in trend lines after 2010 is used to reflect this change in methodolgy. Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].

aCurrent smokers smoked at least 100 cigarettes in their lifetime and smoked “every day” or “some days” now.bDaily smokers smoked “every day” now, or if they smoked “some days,” they smoked on >25 days in the past 30 days.cNondaily smokers smoked “some days” now and smoked on ≤25 days in the past 30 days.dVery light daily smokers are daily smokers who smoked ≤5 cigarettes per day.eVery light nondaily smokers are nondaily smokers who smoked ≤3 cigarettes per day.fInfrequent smokers are nondaily smokers who smoked on ≤8 days in the past 30 days.gSmoking respondent reported that he/she had stopped smoking for more than one day because he/she was trying to quit smoking in the past 12 months.hPoverty status is a ratio of family income to the appropriate poverty threshold (given family size and number of children) defined by the US Census Bureau. “Poor” adults reported a family income below the poverty threshold. “Near poor” adults had a family income of 100–199% of the poverty threshold. “Not poor” adults reported a family income of 200% of the poverty threshold or greater.iLifetime abstainers had fewer than 12 drinks in lifetime; Former drinkers had at least 12 drinks in lifetime, but none in past year; Current light drinkers drank 1–3 drinks per week in past year; Current moderate drinkers drank 4–14 drinks per week for male and 4–7 drinks per week for female; Current heavy drinkers drank >14 drinks per week for male and >7 drinks per week for female.jBinge drinkers drank ≥5 drinks on at least one day in the past 12 months.Note. CI = confidence interval.

BackgroundWe aimed to clarify the relationship between coronavirus disease 2019 (COVID-19) reinfection and basic disease and smoking status.MethodsThe electronic health records of 165,320 patients with COVID-19 from January 1, 2020, to August 27, 2021, were analyzed. Data on age, race, sex, smoking status (never, current, former), and basic disease were analyzed using Cox proportional hazard models.ResultsIn total, 6,133 patients (3.7%) were reinfected. The overall reinfection rate for never, current, and former smokers was 4.2, 3.5, and 5.7%, respectively. Although the risk of reinfection was highest among former smokers aged ≥65 years (7.7% [422/5,460]), the reinfection rate among current smokers aged ≥65 years was 6.2% (341/5,543). Among reinfected patients, the number of basic diseases was higher in former smokers (2.41 ± 1.16) than in current (2.28 ± 1.07, P = 0.07) and never smokers (2.07 ± 1.05, P < 0.001). Former smokers who are older may have been exposed to factors that increase their risk of symptomatic COVID-19 reinfection.

Overview: The QuitNowTXT text messaging program is designed as a resource that can be adapted to specific contexts including those outside the United States and in languages other than English. Based on evidence-based practices, this program is a smoking cessation intervention for smokers who are ready to quit smoking. Although evidence supports the use of text messaging as a platform to deliver cessation interventions, it is expected that the maximum effect of the program will be demonstrated when it is integrated into other elements of a national tobacco control strategy. The QuitNowTXT program is designed to deliver tips, motivation, encouragement and fact-based information via unidirectional and interactive bidirectional message formats. The core of the program consists of messages sent to the user based on a scheduled quit day identified by the user. Messages are sent for up to four weeks pre-quit date and up to six weeks post quit date. Messages assessing mood, craving, and smoking status are also sent at various intervals, and the user receives messages back based on the response they have submitted. In addition, users can request assistance in dealing with craving, stress/mood, and responding to slips/relapses by texting specific key words to the QuitNow. Rotating automated messages are then returned to the user based on the keyword. Details of the program are provided below. Texting STOP to the service discontinues further texts being sent. This option is provided every few messages as required by the United States cell phone providers. It is not an option to remove this feature if the program is used within the US. If a web-based registration is used, it is suggested that users provide demographic information such as age, sex, and smoking frequency (daily or almost every day, most days, only a few days a week, only on weekends, a few times a month or less) in addition to their mobile phone number and quit date. This information will be useful for assessing the reach of the program, as well as identifying possible need to develop libraries to specific groups. The use of only a mobile phone-based registration system reduces barriers for participant entry into the program but limits the collection of additional data. At bare minimum, quit date must be collected. At sign up, participants will have the option to choose a quit date up to one month out. Text messages will start up to 14 days before their specified quit date. Users also have the option of changing their quit date at any time if desired. The program can also be modified to provide texts to users who have already quit within the last month. One possible adaptation of the program is to include a QuitNowTXT "light" version. This adaptation would allow individuals who do not have unlimited text messaging capabilities but would still like to receive support to participate by controlling the number of messages they receive. In the light program, users can text any of the programmed keywords without fully opting in to the program. Program Design: The program is designed as a 14-day countdown to quit date, with subsequent six weeks of daily messages. Each day within the program is identified as either a pre-quit date (Q- # days) or a post-quit date (Q+#). If a user opts into the program fewer than 14 days before their quit date, the system will begin sending messages on that day. For example, if they opt in four days prior to their quit date, the system will send a welcome message and recognize that they are at Q-4 (or four days before their quit date), and they will receive the message that everyone else receives four days before their quit date. As the user progresses throughout the program, they will receive messages outlined in the text message library. Throughout the program, users will receive texts that cover a variety of content areas including tips, informational content, motivational messaging, and keyword responses. The frequency of messages incre

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.

This dataset contains detailed demographic and health-related information for individuals alongside their corresponding medical insurance charges. It includes features such as age, sex, BMI, number of children, smoking status, region, and total insurance cost. This dataset is covered from the USA.

The dataset is ideal for building and evaluating machine learning models that predict healthcare costs based on personal and lifestyle factors.

Dataset Features

1. age: Age of the individual in years.

2. sex: Biological sex of the individual (male or female).

3. BMI: Body Mass Index — the numeric measure of body fat based on height and weight.

4. children: Number of dependent children covered by the insurance plan.

5. smoker: Smoking status of the individual (yes or no).

6. region: Geographic region of the individual within the United States (northeast, northwest, southeast, or southwest).

7. charges: Individual medical insurance cost billed by the insurer.

Distribution

• Format: CSV (Comma-Separated Values)

• Data Volume: Rows: 1,338 records

• 7 Columns: age, sex, BMI, children, smoker, region, charges

• File Size: Approximately 56 KB

Usage

This dataset is ideal for a variety of applications:

Medical Cost Prediction: Train regression models to estimate insurance charges based on demographic and lifestyle factors

Health Economics Research: Analyze how factors like smoking, BMI, and age impact healthcare costs.

Geographic Coverage:

• United States: the dataset includes individuals from four regions: northeast, northwest, southeast, and southwest.

• Time Range: The exact dates of data collection are not specified, but the data reflects typical insurance and demographic patterns observed in recent years.

• Demographics: Includes a diverse range of individuals: Age Range: From 18 to 64 years old Gender: Male and female Lifestyle Factors: Smoking status and BMI Dependents: Number of children covered by the insurance

License

CC0

Who Can Use It

• Data Scientists: For training machine learning models.
• Researchers: For academic or scientific studies.
• Businesses: For analysis, insights, or AI development.

Prevalence is measured as the proportion of daily smokers in each province who were considering quitting. Smoking is a health behaviour that deteriorates health. Smoking is the most important cause of preventable illness, disability and premature death.

In 2023, around 28.6 percent of the population aged 15 years and above in Indonesia were smokers. Smoking prevalence in Indonesia peaked in 2018 at 32.2 percent. To address the widespread prevalence of smoking, the government imposed a tax hike in 2020. Cigarette consumption in Indonesia Despite the Indonesian government's increase in excise duties on cigarettes and tobacco products, smoking among adults remains high, particularly among men. Cultural norms, low prices, and aggressive tobacco marketing significantly challenge efforts to reduce smoking rates. In Indonesia, smoking is deeply embedded in social practices and often begins at a young age. Recent data indicates that Indonesians aged 18 to 59 smoke an average of 12 cigarettes daily, equivalent to one regular-sized pack of cigarettes sold in the country. Tobacco industry in Indonesia The tobacco industry in Indonesia is a vital economic sector, ranking among the world’s leading producers and consumers of tobacco. Indonesia produced over 200,000 metric tons of tobacco annually, with exports to countries such as the Philippines and the United States. This extensive production and export network underscores the industry's importance to Indonesia's economy. The total export value of tobacco and its manufactured products from Indonesia is estimated to be nearly two billion U.S. dollars, highlighting its significant contribution to the nation's economic landscape.

Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches such as we are developing. To begin addressing this need we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response and looked for genes that respond to radiation differently in smokers and non-smokers and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry. Blood from each of 24 different donors was exposed to four doses of ionizing radiation (0 0.1 0.5 or 2 Gy) and analyzed using single-color microarray hybridization. The donors represented equal numbers of male and female smokers (1 or more packs a day) and non-smokers. There are 95 data sets in the study as the sample from one of the female smokers exposed to 2 Gy was lost.

Summary statistics of mean percentage of daily smokers for males and females in 8 U.S. geographic regions, 1999–2012.

This is the data used in the manuscript. This dataset is associated with the following publication: Ghio, A., J. Soukup, J. Mcgee, M. Madden, and C. Esther Jr. Iron concentration in exhaled breath condensates decreases in ever-smokers and COPD patients. Journal of Breath Research. Institute of Physics Publishing, Bristol, UK, 12(4): 046009, (2018).

SRM 3673 Organic Contaminants in Non-Smokers' Urine (Frozen) - This Standard Reference Material (SRM) is intended for use in evaluating analytical methods for the determination of selected hydroxylated polycyclic aromatic hydrocarbons (hydroxylated PAHs) and phthalate, phenol, and volatile organic compound (VOC) metabolites in urine. All of the constituents for which certified and reference values are provided are naturally present in the urine. This data is public in the Certificate of Analysis for this material.

This thesis is based on a prospective cohort study in adolescents and young adults who use the HKU Youth Quitline service in Hong Kong, and a pilot RCT on EMA-based peer counselling for smoking cessation in a sample recruited from the Youth Quitline since May 2023.Data were collected using structured questionnaire by peer counsellors via telephone interviews. The questionnaire includes questions on basic sociodemographic and smoking-related characteristics, including sex, age, marital status, employment status, and educational attainment, smoking status, age at smoking initiation and regular smoking, cigarette consumption per day, nicotine dependence assessed by the Fagerström Test for Nicotine Dependence (FTND), intention to quit, past quit attempts, self-efficacy (perceived importance, confidence, and difficulty of quitting), and withdrawal symptoms. The lifestyle factors (alcohol drinking habits, physical activity), depressive symptoms measured by the Center for Epidemiological Studies Depression (CES-D), and self-esteem were also collected. In addition, we also assessed the use of other tobacco products in past 30 days. In addition, the EMA study collected data on the mental states, smoking cues, and sleep status 4 times a day during each 1-week EMA wave for 2/3 waves.The smoking cessation outcomes were assessed at 3-month and 6-month follow-up. Self-reported 7-day PPA was defined as not smoking during the past 7 days at follow ups. Those who reported no smoking in the past 7 days at 6-month follow-up were invited for biochemical validation test. The biochemically validated abstinence was confirmed by saliva cotinine level ≤ 10 ng/ml or a CO level in expired air ≤ 4 p.p.m. An incentive of HK$300 was given to those who passed the test. Smoking reduction was defined as cigarette consumption reduction by at least 50% at 6-month follow-up compared to baseline ≥50%.

Summary statistics of the mean percent of daily males and females smokers for 38 states and District of Columbia, 1999–2012.