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: Irani... Visit https://dataone.org/datasets/sha256%3Aaa1b4aae69c3399c96bfbf946da54abd8f7642332d12ccd150c42ad400e9699b for complete metadata about this dataset.

Forecast: Unmanufactured Tobacco Gross Production in the US 2024 - 2028 Discover more data with ReportLinker!

About this dataset

Context

The dataset tabulates the population of Port Tobacco Village by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Port Tobacco Village. The dataset can be utilized to understand the population distribution of Port Tobacco Village by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Port Tobacco Village. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Port Tobacco Village.

Key observations

Largest age group (population): Male # 80-84 years (5) | Female # 0-4 years (0). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Port Tobacco Village population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Port Tobacco Village is shown in the following column.
• Population (Female): The female population in the Port Tobacco Village is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Port Tobacco Village for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Port Tobacco Village Population by Gender. You can refer the same here

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 (CNP) 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 CNP 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 CNP 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 CNP at the time of Wave 7. This combined set 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 respondents and augment the analyses of the characteristics of tobacco products used

Analysis of ‘Youth Tobacco Survey ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/youth-tobacco-survey-yts-datae on 28 January 2022.

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

About this dataset

1999-2017. Centers for Disease Control and Prevention (CDC). State Tobacco Activities Tracking and Evaluation (STATE) System. YTS Data. The YTS was developed to provide states with comprehensive data on both middle school and high school students regarding tobacco use, exposure to environmental tobacco smoke, smoking cessation, school curriculum, minors' ability to purchase or otherwise obtain tobacco products, knowledge and attitudes about tobacco, and familiarity with pro-tobacco and anti-tobacco media messages. The YTS uses a two-stage cluster sample design to produce representative samples of students in middle schools (grades 6–8) and high schools (grades 9–12). The data for the STATE System were extracted from Youth Tobacco Surveys from participating states. Tobacco topics included are cigarette smoking prevalence, cigarette smoking frequency, smokeless tobacco products prevalence and quit attempts.

Source: https://catalog.data.gov/dataset/youth-tobacco-survey-yts-data
Last updated at https://catalog.data.gov/organization/hhs-gov : 2021-04-25

This dataset was created by US Open Data Portal, data.gov and contains around 10000 samples along with Stratificationid3, Data Value Footnote Symbol, technical information and other features such as: - Topictypeid - Topictype - and more.

How to use this dataset

• Analyze Submeasureid in relation to Displayorder
• Study the influence of Data Value on Datasource
• More datasets

Acknowledgements

If you use this dataset in your research, please credit US Open Data Portal, data.gov

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

Forecast: Production Volumes of Tobacco Products in the US 2024 - 2028 Discover more data with ReportLinker!

About this dataset

Context

The dataset tabulates the Tobacco township population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Tobacco township. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,267 (51.27% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Tobacco township population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Tobacco township is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Tobacco township is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tobacco township Population by Age. You can refer the same here

US E-Cig Tax Regulations

1995-2016 State Taxation Policies

By Health [source]

About this dataset

This dataset, provided by the Centers for Disease Control and Prevention (CDC) through the State Tobacco Activities Tracking and Evaluation (STATE) System, contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes. It captures various measures of state excise taxes for e-cigarettes implemented over a span of almost two decades. The STATE System stores comprehensive historical data which can be used to track changes in these policies at the state level over time. This dataset includes fields such as location abbreviations, topic descriptions, measure descriptions, provision value, provision description, citations and more that provide valuable insight into understanding how these measures have evolved overtime across states in the US

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information on state-level legislative data on tobacco use prevention and control policies related to e-cigarette taxes from 1995-2016. It includes the following columns: location abbreviations, location descriptions, topic descriptions, measure descriptions, data sources, provision group descriptions, provision descriptions, provision values, citations for the provisions cited in the dataset as well as alternative values for those provisions if they are used. Additionally it contains dates when certain provisions become effective or enacted and also geographic locations of the data which can be used as a helpful reference point.

In order to best use this dataset you should familiarize yourself with its columns and their definitions. This will help you better understand how each element relates to others within the set and give you an idea of what type of analyses can be conducted using it. You should also take note of any relevant comments that may shed light on specific elements or provide additional information not captured in other columns. After understanding the contents of this dataset it is suggested that individuals analyze it according to their individual needs and interests but some general uses may include exploring trends in e-cigarette taxation over time by examining yearly changes in tax rates or seeing how tax regulation varies among states depending on location abbreviations provided in each row entry etc.. With these tools one could potentially make meaningful connections between different variables within this set and gain valuable insights into how US states legislate taxes related to tobacco use prevention methods

Research Ideas

• Analyzing the impact of e-cigarette taxes on usage rates in different states, in order to inform tax policy decisions.
• Examining the differences between enacted and effective dates for legislations by state and across the country, in order to gain a better understanding of how long it takes for new laws to become implemented.
• Tracking changes of e-cigarette regulation over time and studying how they correlate with measures such as number of youth users or youth perception on risk associated with e-cigarettes by state

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: CDC_STATE_System_E-Cigarette_Legislation_-_Tax.csv | Column name | Description | |:-----------------------|:------------------------------------------------------------------| | YEAR | Year of the policy (Integer) ...

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Adults included in this indicator are current cigarette smokers. Current smokers are defined as adults who smoked at least 100 cigarettes in their lifetime and currently smoke.Tobacco use is a leading preventable cause of premature death and disability. Cities and communities can curb tobacco use by adopting policies to regulate tobacco retail and reduce exposure to secondhand smoke in outdoor public spaces, such as parks, restaurants, or in multi-unit housing.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

CDC Maternal Health Survey

Attitudes and Experiences Before, During, and After Pregnancy

By Health [source]

About this dataset

The Centers for Disease Control and Prevention (CDC) is proud to present PRAMS, the Pregnancy Risk Assessment Monitoring System. This survey provides valuable insights and analysis on maternal health, mindset, and experiences pre-pregnancy through postpartum phase. Statistically representative data is gathered from mothers all over the United States concerning issues such as abuse, alcohol use, contraception, breastfeeding, mental health, obesity and many more.

This survey provides an invaluable source of information which is key in targeting areas that need improvement when it comes to maternal wellbeing. Armed with PRAMS data state health officials are able to work towards promoting a healthy environment for mothers and their babies during this important period of life. Rich in data points ranging from smoking exposure to infant sleep behavior trends can be identified across states as well as nationally with this unique system supported by CDC's partnership with state health departments.

Here you will find a-mazing datasets containing columns such like Year or LocationAbbr or Response allowing you analyze some really meaningful stuff like: Are women in certain parts of the US more likely compared to others to breastfeed? What about rates at which pregnant mothers take prenatal care? Dive into the 2019 CDC PRAMStat dataset today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

In order to make full use of this dataset it’s important that you understand what each column contains so that you can extract the most relevant data for your purposes. Here are some tips for understanding how to maximize this dataset: - Look through each column carefully – take note of which columns contain numerical information (Data_Value_Unit), categorical responses (Response) or location descriptions (Location Desc). - Make sure that you are aware of any standard errors that may be associated with data values (Data_Value_Std_Err). - It’s useful to know the source(DataSource)of your data so if possible check out who has collected it.
- Check what classifications have been used in BreakOut columns – this can give additional insight into how subjects were divided up within datasets.
- Understand how pregnancies were grouped together geographically by taking a look at LocationAbbr and Geolocation columns - understanding where surveys have been done can help break down regional differences in responses.
With these steps will help you navigate through your dataset so that you can accurately interpret questions posed by pregnant women from different locations across the U.S.

Research Ideas

• Using this dataset, public health officials could analyze maternal attitudes and experiences over a period of time to develop targeted strategies to improve maternal health.
• This dataset can be used to create predictive models of maternal behavior based on the amount of prenatal care received and other factors such as alcohol use, sleep behavior and tobacco use.
• Analyzing this dataset would also allow researchers to identify trends in infant wellbeing outcomes across various states/municipalities with different policies/interventions in place which can then be replicated in other areas with similar characteristics

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description ...

About this dataset

Context

The dataset tabulates the Port Tobacco Village population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Port Tobacco Village. The dataset can be utilized to understand the population distribution of Port Tobacco Village by age. For example, using this dataset, we can identify the largest age group in Port Tobacco Village.

Key observations

The largest age group in Port Tobacco Village, MD was for the group of age 80 to 84 years years with a population of 5 (100%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Port Tobacco Village, MD was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Port Tobacco Village is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Port Tobacco Village total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Port Tobacco Village Population by Age. You can refer the same here

Forecast: Production of Food, Beverages and Tobacco in the US 2024 - 2028 Discover more data with ReportLinker!

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

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 data set features businesses/individuals holding a DCA license so that they may legally operate in New York City. Note: Sightseeing guides and temporary street fair vendors are not included in this data set.
*Due to COVID-19 pandemic, DCA extended certain license expiration dates and renewal application deadlines which are not reflected in this data set. For more information, visit nyc.gov/BusinessToolbox.

Global Unmanufactured Tobacco Gross Production Share by Country (Thousand US Dollars PPP = 2004–2006), 2023 Discover more data with ReportLinker!

The Digital Geologic-GIS Map of the Port Tobacco Quadrangle, Maryland is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (poto_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (poto_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (poto_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (thst_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (thst_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (poto_geology_metadata_faq.pdf). Please read the thst_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Maryland Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (poto_geology_metadata.txt or poto_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

About this dataset

Context

The dataset tabulates the population of Tobacco township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Tobacco township. The dataset can be utilized to understand the population distribution of Tobacco township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Tobacco township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Tobacco township.

Key observations

Largest age group (population): Male # 70-74 years (165) | Female # 60-64 years (146). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Tobacco township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Tobacco township is shown in the following column.
• Population (Female): The female population in the Tobacco township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Tobacco township for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tobacco township Population by Gender. You can refer the same here

Objectives: Define the role of increasing cannabis availability on population mental health (MH).

Methods. Ecological cohort study of National Survey of Drug Use and Health (NSDUH) geographically-linked substate-shapefiles 2010-2012 and 2014-2016 supplemented by five-year US American Community Survey. Drugs: cigarettes, alcohol abuse, last-month cannabis use and last-year cocaine use. MH: any mental illness, major depressive illness, serious mental illness and suicidal thinking. Data analysis: two-stage and geotemporospatial methods in R.

Results: 410,138 NSDUH respondents. Average response rate 76.7%. When all drug exposure, ethnicity and income variables were combined in final geospatiotemporal models tobacco, alcohol cannabis exposure, and various ethnicities were significantly related to all four major mental health outcomes. Cannabis exposure alone was related to any mental illness (β-estimate= -3.315+0.374, P<2.2x10-16), major depressive episode (β-estimate= -3.712+0.454, P=3.0x10-16), serious mental illness (SMI, β-estimate= -3.063+0.504, P=1.2x10-9), suicidal ideation (β-estimate= -3.013+0.436, P=4.8x10-12) and with more significant interactions in each case (from β-estimate= 1.844+0.277, P=3.0x10-11). Geospatial modelling showed a monotonic upward trajectory of SMI which doubled (3.62% to 7.06%) as cannabis use increased. Extrapolated to whole populations cannabis decriminalization (4.35+0.05%, Prevalence Ratio (PR)=1.035(95%C.I. 1.034-1.036), attributable fraction in the exposed (AFE)=3.28%(3.18-3.37%), P<10-300) and legalization (4.66+0.09%, PR=1.155(1.153-1.158), AFE=12.91% (12.72-13.10%), P<10-300) were associated with increased SMI vs. illegal status (4.26+0.04%).

Conclusions: Data show all four indices of mental ill-health track cannabis exposure and are robust to multivariable adjustment for ethnicity, socioeconomics and other drug use. MH deteriorated with cannabis legalization. Together with similar international reports and numerous mechanistic studies preventative action to reduce cannabis use-exposure is indicated.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Tobacco Township, Michigan, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 25,949, while the mean income for the highest quintile (20% of households with the highest income) is 214,797. This indicates that the top earners earn 8 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 355,328, which is 165.43% higher compared to the highest quintile, and 1369.33% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Tobacco township median household income. You can refer the same here