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].

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

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].

The Helping Young Smokers Quit (HYSQ) initiative was a multi-phase project that addressed the critical need to disseminate effective, developmentally appropriate cessation programs for young smokers. Phase I identified and described tobacco treatment programs available for youth in the United States, Phase II evaluated smoking secession programs tailored for youth to help understand what works, and Phase III identified factors associated with program sustainability. Phase II collected data from five sources: (1) program participants, (2) program providers, (3) program curricula, (4) organizational leaders, and (5) community leaders and community ordinances. Program participants were interviewed at baseline, end-of program, 6-month follow-up, and 12-month follow-up. Topics covered by the interviews include age, gender, race, Hispanic origin, language spoken at home, employment, income, religiosity, school enrollment, education level, school grades, height, weight, extracurricular activities, recreation, sports, exercise, aspirations after high school, psychological well-being, alcohol consumption, cigarette use and use of other tobacco products, attitudes about smoking, plans to stop/continue smoking, attempts to quit smoking, reasons for participating in the program, topics/issues covered by the program, opinions about the program, and smoking experience since the beginning of the program. In addition, for each follow-up survey, the participants provided a breath sample for carbon monoxide analysis to validate self-reported quit status. After the last session of each program delivery, the program providers, such as program leaders and cessation counselors, were interviewed about the content and delivery of the program and the reactions of the participants and themselves to the program as delivered. The program providers also kept attendance records. Curriculum content was abstracted from program manuals and other materials used in each program. Organizational leaders of the organizations that offered the programs were surveyed about various aspects of each organization, including the organization's smoking cessation program and the organization's mission, general operations, and smoking-related policies and practices. Community-level information was collected in two ways: (1) interviews of community leaders representing local health departments, school boards, and juvenile justice offices, and (2) archival research of public ordinances relevant to tobacco and control policies. Nine data files/datasets constitute the data. Datasets 1-4 contain the participant questionnaire data, carbon monoxide measurement data, and program attendance data. Dataset 5 comprises information about each program and its curriculum, some information about the community in which the program was located, and summary data about enforcement of tobacco-related ordinances. Dataset 6 contains information about about the program providers and each program delivery, including recruitment, logistics, content, and the reactions of providers and participants. Dataset 7 covers administrative aspects of the smoking cessation programs and each offering organization's mission, general operations, and smoking-related policies and norms. Dataset 8 contains information about local and state-level tobacco-related ordinances for every state and local jurisdiction where each program was located, and Dataset 9 condenses the information in Dataset 8 into one summary record for each community. The unit of observation for Datasets 1-4 is the participant, for Datasets 5 and 7 the smoking cessation program/offering organization, for Dataset 6 the program delivery/program cohort, for Dataset 8 the ordinance, and for Dataset 9 the community.

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

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.

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.

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

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.

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.

Cigarette use intensity and quit intentions according to past month use (yes or no) of electronic cigarettes, 2011–2015.

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.gLifetime 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.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.*P < .05;**P < .01;***P < .001Note. AOR = adjusted odds ratio; CI = confidence interval.

BackgroundSmoking is a global public health concern, with approximately 1,245 billion smokers worldwide. It is associated with a range of health complications, including cardiovascular and respiratory diseases. Osteoporosis, characterized by reduced bone density and deterioration of bone tissue, has a global prevalence of 18.3%, with higher rates in women over the age of 50. Smoking has been recently associated with osteoporosis, potentially due to shared metabolic disorders or personal habits. This study aimed to investigate the association between smoking and osteoporosis in relation to all-cause mortality in a cohort from the United States.MethodsData were sourced from the National Health and Nutrition Examination Survey (NHANES) database, which focuses on individuals aged 20 years and older from 2005–2010, 2013–2014, and 2017–2018, where femoral neck bone density testing was conducted. The participants were categorized on the basis of their self-reported smoking status and bone mineral density (BMD) measurements, following the World Health Organization criteria for osteoporosis. The covariates included age, sex, race, alcohol consumption, BMI, blood glucose levels, and other health indicators. Statistical analysis included ANOVA and chi-square tests for baseline characteristics, Kaplan–Meier survival analysis, and multivariate Cox regression analysis to assess hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality. We divided the patients into four different groups via a cross-classification method on the basis of smoking status and whether they had osteoporosis.ResultsThis study included 19,400 participants, with significant differences in baseline characteristics across 4 groups (S-/OP+: nonsmokers with osteoporosis; S+/OP-: smokers without osteoporosis; S-/OP-: nonsmokers without osteoporosis; S+/OP+: smokers with osteoporosis). The overall average age was 53.1 years, and women accounted for 49.6% of the total population. The mortality rate due to all factors in the total population was 13.1%, with the highest S+/OP+ mortality rate. Participants with both a smoking history and osteoporosis had a 146% increase in all-cause mortality (HR: 2.46, 95% CI: 2.12–2.87) even after adjusting for confounding factors. The relative excess risk due to interaction (RERI) suggested a lack of statistical significance, whereas the attributable proportion (AP) indicated a synergistic effect between smoking and osteoporosis.ConclusionsThis cohort study highlights the importance of managing and preventing smoking and osteoporosis to reduce the risk of all-cause mortality. The findings provide preliminary evidence of a synergistic effect between smoking and osteoporosis on all-cause mortality risk, emphasizing the need for proactive strategies for smoking cessation and close monitoring of risk factors in individuals with both conditions.

Clinical and demographic characteristics of heavy smokers by QCT disease-dominant phenotype.