U.S. Tobacco Use Data

Prevalence and Trends by State

By Health [source]

About this dataset

This dataset provides insight into the prevalence and trends in tobacco use across the United States. By breaking down this data by state, you can see how tobacco has been used and changed over time. Smoking is a major contributor to premature deaths and health complications, so understanding historic usage rates can help us analyze and hopefully reduce those negative impacts. Drawing from the Behavioral Risk Factor Surveillance System, this dataset gives us an unparalleled look at both current and historical smoking habits in each of our states. With this data, we can identify high risk areas and track changes throughout the years for better health outcomes overall

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How to use the dataset

This dataset contains information on the prevalence and trends of tobacco use in the United States. The data is broken down by state, and includes percentages of smokers, former smokers, and those who have never smoked. With this dataset you can explore how smoking habits have changed over time as well as what regions of the country have seen more or less consistent smoking trends.

To begin using this dataset, you will first want to familiarize yourself with the columns included within it and their associated values. There is a “State” column that provides the US state for which each row refers to; there are also columns detailing percentages for those who smoke every day (Smoke Everyday), some days (Smoke Some Days), previously smoked (Former Smoker) and those who have never smoked (Never Smoked). The “Location 1” column indicates each geographic region that falls into one of either four US census divisions or eight regions based upon where each state lies in relation to one another.

Once you understand the data presented within these columns, there are a few different ways to begin exploring how tobacco use has changed throughout time including plotting prevalence data over different periods such as decades or specific years; compiling descriptive statistics such as percentiles or mean values; contrasting between states based on any relevant factors such as urban/rural population size or economic/political standing; and lastly looking at patterns developing throughout multiple years via various visualisations like box-and-whisker plots amongst other alternatives.

This wide set of possibilities makes this dataset interesting enough regardless if you are looking at regional differences across single points in time or long-term changes regarding national strategies around reducing nicotine consumption. With all its nuances uncovered hopefully your results can lead towards further research uncovering any aspect about smoking culture you may find fascinating!

Research Ideas

• Comparing regional and state-level smoking rates and trends over time.
• Analyzing how different demographics are affected by state-level smoking trends, such as comparing gender or age-based differences in prevalence and/or decreasing or increasing rates of tobacco use at the regional level over time.
• Developing visualization maps that show changes in tobacco consumption prevalence (and related health risk factors) by location on an interactive website or tool for public consumption of data insights from this dataset

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: BRFSS_Prevalence_and_Trends_Data_Tobacco_Use_-_Four_Level_Smoking_Data_for_1995-2010.csv | Column name | ...

The global smoking prevalence in was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the ****** consecutive decreasing year, the smoking prevalence is estimated to reach ***** percent and therefore a new minimum in 2029. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the smoking prevalence in countries like North America and Caribbean.

US Tobacco Use Prevalence

US Tobacco Use Prevalence by Year, State, Type, and Age

By Throwback Thursday [source]

About this dataset

This dataset contains comprehensive information on tobacco use in the United States from 2011 to 2016. The data is sourced from the CDC Behavioral Risk Factor Survey, a reliable and extensive survey that captures important data about tobacco use behaviors across different states in the United States.

The dataset includes various key variables such as the year of data collection, state abbreviation indicating where the data was collected, and specific tobacco types explored in the survey. It also provides valuable insight into the prevalence of tobacco use through quantitative measures represented by numeric values. The unit of measurement for these values, such as percentages or numbers, is included as well.

Moreover, this dataset offers an understanding of how different age groups are affected by tobacco use, with age being categorized into distinct groups. This ensures that researchers and analysts can assess variations in tobacco consumption and its associated health implications across different age demographics.

With all these informative attributes arranged in a convenient tabular format, this dataset serves as a valuable resource for investigating patterns and trends related to tobacco use within varying contexts over a six-year period

How to use the dataset

Introduction:

Step 1: Familiarize Yourself with the Columns

Before diving into any analysis, it is important to understand the structure of the dataset by familiarizing yourself with its columns. Here are the key columns in this dataset:

• Year: The year in which the data was collected (Numeric)
• State Abbreviation: The abbreviation of the state where the data was collected (String)
• Tobacco Type: The type of tobacco product used (String)
• Data Value: The percentage or number representing prevalence of tobacco use (Numeric)
• Data Value Unit: The unit of measurement for data value (e.g., percentage, number) (String)
• Age: The age group to which the data value corresponds (String)

Step 2: Determine Your Research Questions or Objectives

To make effective use of this dataset, it is essential to clearly define your research questions or objectives. Some potential research questions related to this dataset could be:

• How has tobacco use prevalence changed over time?
• Which states have the highest and lowest rates of tobacco use?
• What are the most commonly used types of tobacco products?
• Is there a correlation between age group and tobacco use?

By defining your research questions or objectives upfront, you can focus your analysis accordingly.

Step 3: Analyzing Trends Over Time

To analyze trends over time using this dataset: - Group and aggregate relevant columns such as Year and Data Value. - Plot the data using line graphs or bar charts to visualize the changes in tobacco use prevalence over time. - Interpret the trends and draw conclusions from your analysis.

Step 4: Comparing States

To compare states and their tobacco use prevalence: - Group and aggregate relevant columns such as State Abbreviation and Data Value. - Sort the data based on prevalence rates to identify states with the highest and lowest rates of tobacco use. - Visualize this comparison using bar charts or maps for a clearer understanding.

Step 5: Understanding Tobacco Types

To gain insights into different types of tobacco products used: - Analyze the Tobacco

Research Ideas

• Analyzing trends in tobacco use: This dataset can be used to analyze the prevalence of tobacco use over time and across different states. It can help identify patterns and trends in tobacco consumption, which can be valuable for public health research and policy-making.
• Assessing the impact of anti-smoking campaigns: Researchers or organizations working on anti-smoking campaigns can use this dataset to evaluate the effectiveness of their interventions. By comparing the data before and after a campaign, they can determine whether there has been a decrease in tobacco use and if specific groups or regions have responded better to the campaign.
• Understanding demographic factors related to tobacco use: The dataset includes information on age groups, allowing for analysis of how different age demographics are affected by tobacco use. By examining data value variations across age groups, researchers can gain insights into which populations are most vulnerable to smoking-related issues and design targeted prevention programs an...

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.

2005-2009. SAMMEC - Smoking-Attributable Mortality, Morbidity, and Economic Costs. Smoking-attributable mortality (SAM) is the number of deaths caused by cigarette smoking based on diseases for which the U.S. Surgeon General has determined that cigarette smoking is a causal factor.

US States Tobacco Use Prevalence

1996-2010 Survey Data

By Health [source]

About this dataset

This dataset from the Centers for Disease Control and Prevention (CDC) provides state-based surveillance information related to tobacco use among American adults from 1996 to 2010. It contains data on modifiable risk factors for chronic diseases and other leading causes of death obtained from annual BRFSS surveys conducted in participating states.

The dataset focuses on key topics such as cigarette smoking status, prevalence by demographics, frequency, and quit attempts. The metrics collected are important indicators of public health efforts in tobacco prevention, control and cessation programs at the state level.

With this dataset you can explore how people perceive smoking differently across geographical areas as well as their socio-economic backgrounds like gender identity, race or ethnicity, educational level or life stage. Analyzing this data will give us valuable insights into the impact of tobacco consumption in our society today and help create more effective public health interventions tailored to local needs

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How to use the dataset

This dataset can be used to study the prevalence of tobacco use in different US states in the period 1996-2010. The dataset contains information on cigarette smoking status, prevalence by demographics, frequency, and quit attempts.

In order to begin exploring this dataset it is recommended that one first understand the column headers and their corresponding values. This can be done by familiarizing oneself with the included data dictionary that defines each column's name and description.

Next it is recommended to familiarize oneself with the data types contained in the columns. Depending on what type of query you are wanting to make some columns may need conversion from one type to another for better results when performing a query. Some common types found within this dataset include integers (whole numbers), strings (text) and floats (decimals).

Once you have familiarized yourself with both the columns and data types it is now a good time to start considering which questions you want answer related to tobacco use in US states during this period of time. Consider which variables might provide valuable insights into your analysis such as age, gender, race etc., as well as other variables such as location or year that could add more complexity or context understanding into your analysis. Assuming that your desired questions have been determined you can begin querying your data using methods supported by whichever language or platform you are choosing work with such us SQL or Python Pandas Dataframes etc.. This will allow manipulation of all relevant variables according get useful insights out of them related back tobaccos use in US states during this specific period.

Finally when doing an analysis on any given topic its helpful no compare ones findings between multiple datasets if possible so consider obtaining any other datasets relevant top toxins use over a similar timespan which could be compared against these findings if available

Research Ideas

• Identifying and targeting high-risk locations for tobacco use prevention efforts by analyzing the prevalence of different forms of tobacco use in different states.
• Examining patterns of tobacco use among different demographic groups (gender, age, race, etc.) to design better tailored interventions for tobacco cessation.
• Comparing quit attempt rates with smoking frequency and prevalence across states to understand the effectiveness of smoke-free laws and policies that have been enacted in recent years

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:-----------------------------------------------| | YEAR | Year of survey (Integer) | | LocationAbbr | Abbreviation of the state (String) | | LocationDesc | Full name of the state (String) | | TopicType | Type of topic (String) | | TopicDesc | Description of the topic (String) | | MeasureDesc | Description of ...

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.

Estimate of current tobacco smoking prevalence (age-standardized) (%)

  Dataset Description

This dataset provides information on 'Estimate of current tobacco smoking prevalence' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: age-standardized

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/estimate-of-current-tobacco-smoking-prevalenceby-s-for-african-countries.

Daily tobacco smoking among adults (%)

  Dataset Description

This dataset provides information on 'Daily tobacco smoking among adults' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.

Index: Year… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/daily-tobacco-smoking-among-adultsby-sex-for-african-countries.

Current tobacco smoking among adults (%)

  Dataset Description

This dataset provides information on 'Current tobacco smoking among adults' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.

Index:… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/current-tobacco-smoking-among-adultsby-sex-for-african-countries.

Current tobacco smoking among adolescents (%)

  Dataset Description

This dataset provides information on 'Current tobacco smoking among adolescents' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: %

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/current-tobacco-smoking-among-adolescentsby-sex-for-african-countries.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Tobacco township. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Tobacco township. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Tobacco township, the median household income stands at $93,977 for householders within the 45 to 64 years age group, followed by $67,344 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $47,500.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific 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 median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Tobacco township 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 Tobacco township. The dataset can be utilized to understand the population distribution of Tobacco township by age. For example, using this dataset, we can identify the largest age group in Tobacco township.

Key observations

The largest age group in Tobacco Township, Michigan was for the group of age 60-64 years with a population of 313 (13.51%), according to the 2021 American Community Survey. At the same time, the smallest age group in Tobacco Township, Michigan was the 85+ years with a population of 40 (1.73%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Tobacco township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Tobacco township 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 Tobacco township Population by Age. You can refer the same here

Comprehensive dataset containing 15,357 verified Smoke shop businesses in United States with complete contact information, ratings, reviews, and location data.

This data package contains dataset on prevalence rates of health conditions and diseases like obesity, diabetes and hearing loss and health risk factors for diseases like tobacco, alcohol and drug use.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Age-standardized prevalence of current tobacco use among persons aged 15 years and older (%)





  Dataset Description

This dataset provides country-level data for the indicator "3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older (%)" across African nations, sourced from the World Health Organization's (WHO) data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/tobacco-use-for-african-countries.

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

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 65-69 years with a population of 1 (50.00%), according to the 2021 American Community Survey. At the same time, the smallest age group in Port Tobacco Village, MD was the 0-4 years with a population of 0 (0.00%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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

US National Cardiovascular Disease

Indicators and Risk Factors from 2001-Present

By US Open Data Portal, data.gov [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to use this dataset

• Accessing the Data: To access this dataset you can visit the website Data.cdc.gov where it is publicly available or download it directly from Kaggle at [https://www.kaggle.com/cdc/us-national-cardiovascular-disease].
• Exploring the data: There are 20 columns/variables that make up this dataset which include Year, LocationAbbr,LocationDesc,DataSource PriorityArea1 through PriorityArea4,CategoryTopicIndicatorData_Value_TypeData_Value_UnitData_Value_Alt FootnoteSymbol BreakOutCategory GeoLocation etc.(see above for full list). You can explore the data however you want by looking at one variable or multiple variables simultaneously in order to gain insight about CVDs in America such as their rates across different locations over years or prevalence of certain risk factors among different age groups and gender etc . 3 . The Uses of This Dataset: This dataset can be used by researchers who are interested in improving our understanding of CVDs in America through accessing its vital statistics such as assessing disease burden and monitoring trends over time across different population subgroups etc., health authorities attempting to publicize vital health related knowledge via data dissemination tactics such as outreach programs or policy makers who intend on informing community level interventions based upon insights extracted from this powerful tool For example - Someone may look at a comparison between smoking prevalence between males & females within one state countrywide or they could further investigate that comparison into doing a time series analysis looking at smoking prevalence trends since 2001 onwards across both genders nationally until present day

Research Ideas

• Creating a real-time cardiovascular disease surveillance system that can send updates and alert citizens about risks in their locale.
• Generating targeted public health campaigns for different demographic groups by drawing insights from the dataset to reach those most at risk of CVDs.
• Developing an app or software interface to allow users to visualize data trends around CVD prevalence and risk factors between different locations, age groups and ethnicities quickly, easily and accurately

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------------| | Year | Year of the survey. (Integer) | | LocationAbbr | Abbreviation of the location. (String) | | LocationDesc | Description of the location. (String) | | DataSource | Source of the data. (String) | | PriorityArea1 | Priority area 1. (String) | | PriorityArea2 | Priority area 2. (String) | | PriorityArea3 | Priority area 3. (String) | | PriorityArea4 | Priority area 4. (String) | | Category | Category of the data value type. (String) | | Topic | Topic related to the indicator of the data value unit. (String) | | Indicator | Indicator of the data value unit. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Unit | Unit of the data value. (String) | | Data_Value_Alt | Alternative value of the data value. (Float) | | Data_Value_Footnote_Symbol | Footnote symbol of the data value. (String) | | Break_Out_Category | Break out category of the data value. (String) | | GeoLocation | Geographic location associated with the survey d...