US Tobacco Use Trends by Age and State

Age and State-wise Trends in US Tobacco Use 2011-2016

By Throwback Thursday [source]

About this dataset

The US Tobacco Use 2011-2016 dataset provides comprehensive information on tobacco use trends in the United States from 2011 to 2016. The data is derived from the CDC Behavioral Risk Factor Survey, which collects data on tobacco use across different age groups and states. The dataset includes variables such as age group, year of data collection, type of tobacco product used, state abbreviation where the data was collected, and the corresponding percentage or number representing the tobacco use data. Additionally, it specifies the unit of measurement for the data value (e.g., percentage or number). This dataset aims to offer valuable insights into patterns of tobacco use in different demographic segments and geographical locations within the United States over a six-year period

How to use the dataset

Step 1: Familiarize yourself with the columns: - Year: Represents the year in which the data was collected. - State Abbreviation: Indicates the abbreviation of the state where the data was collected. - Tobacco Type: Specifies the type of tobacco product used. - Data Value: Represents either a percentage or a number that represents tobacco use data. - Data Value Unit: Indicates whether the measurement is a percentage or a number. - Age Group: Specifies which age group corresponds to each piece of tobacco use data.

Step 2: Identify your area of interest: Consider what specific information you are looking for within this dataset. For example, if you want to examine trends in cigarette smoking among young adults (age group), select relevant columns like Year, State Abbreviation, Data Value (percentage/number), etc. By narrowing down your focus, you can analyze specific trends efficiently.

Step 3: Filter and sort your data: Use filtering features provided by spreadsheet software or coding languages (e.g., Python) to extract only relevant information based on your area of interest. You can filter by year(s), state(s), age group(s), or type(s) of tobacco product used using logical operators such as equal (=) and not equal (!=). This way, you can obtain a subset of data that meets your criteria for analysis conveniently.

Step 4: Analyze trends over time: Utilize line charts or bar graphs to visualize changes in tobacco use percentages or numbers over the years. This will allow you to identify any significant patterns or fluctuations, observing whether there are any consistent trends across different states or age groups.

Step 5: Compare tobacco use between states: To assess the differences in tobacco use across various states, aggregate and compare the data using statistical measures such as averages, medians, and standard deviations. By identifying states with higher or lower tobacco use rates, you can gain insights into potential factors affecting these patterns (e.g., state-specific regulations, cultural norms).

Step 6: Explore variations by age group: Investigate how tobacco use varies among different age groups. Compare percentages/

Research Ideas

• Analyzing trends in tobacco use by age and state: This dataset provides information on tobacco use in the United States from 2011 to 2016, allowing for the analysis of trends over time and differences between states. Researchers or policymakers can use this information to examine changes in tobacco consumption rates and identify patterns or factors influencing tobacco use across different age groups and states.
• Comparing the effectiveness of tobacco control measures: With this dataset, it is possible to assess how different tobacco control measures implemented by states have impacted tobacco consumption rates. By comparing data on tobacco use with specific policies, such as smoke-free laws or increased taxation, researchers can evaluate the effectiveness of these interventions and guide future public health initiatives.
• Investigating disparities in tobacco use: By examining data on age, state, and type of tobacco product used, it is possible to explore disparities in smoking prevalence across different demographic groups and geographic areas. This dataset can be used to identify populations that are more susceptible to smoking or are experiencing higher rates of cigarette usage compared to other groups. This information can inform targeted interventions aimed at reducing these disparities and promoting healthier behaviors among vulnerable populations

Acknowledgements

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

...

The smoking prevalence in the United States 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 Canada and Mexico.

United States US: Smoking Prevalence: Males: % of Adults data was reported at 24.600 % in 2016. This records a decrease from the previous number of 25.100 % for 2015. United States US: Smoking Prevalence: Males: % of Adults data is updated yearly, averaging 26.800 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 34.500 % in 2000 and a record low of 24.600 % in 2016. United States US: Smoking Prevalence: Males: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking, male is the percentage of men ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data was reported at 21.800 % in 2016. This records a decrease from the previous number of 22.300 % for 2015. United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data is updated yearly, averaging 23.900 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 31.400 % in 2000 and a record low of 21.800 % in 2016. United States US: Smoking Prevalence: Total: % of Adults: Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking is the percentage of men and women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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].

Smoking rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States.” The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the smoking rate of a demographically similar Census Tract to one in Allegheny County to compute a smoking rate.

Comparing the *** selected regions regarding the smoking prevalence , Myanmar is leading the ranking (***** percent) and is followed by Serbia with ***** percent. At the other end of the spectrum is Ghana with **** percent, indicating a difference of ***** percentage points to Myanmar. Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke on a daily basis. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** 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).

Smoking trends broken down by gender and country since 1980

Data Dictionary

Acknowlegement

Original Data

CORGIS Dataset Project

Foto von Andres Siimon auf Unsplash

US: Smoking Prevalence: Females: % of Adults data was reported at 19.100 % in 2016. This records a decrease from the previous number of 19.600 % for 2015. US: Smoking Prevalence: Females: % of Adults data is updated yearly, averaging 21.100 % from Dec 2000 (Median) to 2016, with 9 observations. The data reached an all-time high of 28.400 % in 2000 and a record low of 19.100 % in 2016. US: Smoking Prevalence: Females: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of smoking, female is the percentage of women ages 15 and over who currently smoke any tobacco product on a daily or non-daily basis. It excludes smokeless tobacco use. The rates are age-standardized.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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

BackgroundSmoking tobacco reduces lung function. African Americans have both lower lung function and decreased metabolism of tobacco smoke compared to European Americans. African ancestry is also associated with lower pulmonary function in African Americans. We aimed to determine whether African ancestry modifies the association between smoking and lung function and its rate of decline in African Americans. Methodology/Principal FindingsWe evaluated a prospective ongoing cohort of 1,281 African Americans participating in the Health, Aging, and Body Composition (Health ABC) Study initiated in 1997. We also examined an ongoing prospective cohort initiated in 1985 of 1,223 African Americans in the Coronary Artery Disease in Young Adults (CARDIA) Study. Pulmonary function and tobacco smoking exposure were measured at baseline and repeatedly over the follow-up period. Individual genetic ancestry proportions were estimated using ancestry informative markers selected to distinguish European and West African ancestry. African Americans with a high proportion of African ancestry had lower baseline forced expiratory volume in one second (FEV1) per pack-year of smoking (−5.7 ml FEV1/ smoking pack-year) compared with smokers with lower African ancestry (−4.6 ml in FEV1/ smoking pack-year) (interaction P value = 0.17). Longitudinal analyses revealed a suggestive interaction between smoking, and African ancestry on the rate of FEV1 decline in Health ABC and independently replicated in CARDIA. Conclusions/SignificanceAfrican American individuals with a high proportion of African ancestry are at greater risk for losing lung function while smoking.

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...