Adult Smoking

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Percentage of adults who are current smokers (age-adjusted). The 2024 Annual Data Release used data from 2021 for this measure.

Each year, approximately 480,000 premature deaths can be attributed to smoking.1 Cigarette smoking is identified as a cause of various cancers, cardiovascular disease, and respiratory conditions, as well as low birthweight and other adverse health outcomes. Measuring the prevalence of tobacco use in the population can alert communities to potential adverse health outcomes and can be valuable for assessing the need for cessation programs or the effectiveness of existing tobacco control programs. A study evaluating the reliability and validity of the self-reported Behavioral Risk Factor Surveillance System (BRFSS) measures found high reliability and validity for the “current smoker” responses, confirming that they are a fairly accurate portrayal of the population’s smoking behavior.2 The term “tobacco” refers to commercial tobacco, not ceremonial or traditional tobacco.

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Data and methods

Data Source

Behavioral Risk Factor Surveillance System

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random digit dial (RDD) telephone survey that is conducted annually in all states, the District of Columbia, and United States territories. Data obtained from the BRFSS are representative of each state’s total non-institutionalized population over 18 years of age and have included more than 400,000 annual respondents with landline telephones or cellphones since 2011. Data are weighted using iterative proportional fitting (also called "raking") methods to reflect population distributions. Data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators in the Health Snapshots and downloadable datasets. HRQoL measures are age-adjusted to the 2000 U.S. standard population. 

Prior to the 2016 Annual Data Release, up to seven survey years of landline only BRFSS data were aggregated to produce county estimates. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. For the 2016 Annual Data Release and beyond, the CDC produced county estimates using single-year BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex, and race/ethnicity, combined with county-level poverty, as well as county- and state-level contextual effects.1 To produce estimates for those counties where there were no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census Vintage population estimates. CDC used a parametric bootstrapping method to produce standard errors and confidence intervals for those point estimates. This estimation methodology was validated for all U.S. counties, including those with no or small (< 50 respondents) samples.2 This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S.  

For the 2021 Annual Data Release, the CDC has updated their modeling procedure for producing small-area estimates. With the PLACES project, a multilevel statistical modeling framework using multilevel regression and poststratification (MRP) is performed for small-area estimation that links BRFSS data with high spatial resolution population demographic and socioeconomic data from the Census’ American Community Survey (ACS). The CDC has performed internal and external validation studies, which confirm strong consistency between their model-based estimates and the direct BRFSS survey estimates at both the state and county levels. For more technical information on the PLACES modeling procedure, please see their website.3 

1Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033. 

2Zhang X, Holt JB, Yun, S, Lu H, Greenlund K, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. American Journal of Epidemiology 2015;182(2):127-137. 

3PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021.

Key Measure Methods

Adult Smoking is a percentage

Adult Smoking is the percentage of the adult population in a county who both report that they currently smoke every day or some days and have smoked at least 100 cigarettes in their lifetime.

Adult Smoking estimates are age-adjusted

Age is a non-modifiable risk factor, and certain health behaviors may be associated with age. We report an age-adjusted rate in order to fairly compare counties with differing age structures.

The method for calculating Adult Smoking has changed

Prior to the 2016 Annual Data Release, the Centers for Disease Control and Prevention's (CDC) BRFSS county-level estimates were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Annual Data Release, CDC has provided single-year modeled county-level estimates which include both landline and cell phone users. Beginning with the 2021 Annual Data Release, CDC introduced an updated modeling procedure to produce small-area estimates. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community. This measure has been age-adjusted since the 2021 Annual Data Release. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community.

Adult Smoking is created using statistical modeling

Surveys collect information about a limited portion of a population. Statistical modeling can be used to predict how people who share certain characteristics with those surveyed may have responded to the survey. Modeling can increase the power of survey data by generating more stable estimates for places with small numbers of residents or survey responses. The Adult Smoking estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.3 

There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to its methods. 

Caution should be used when comparing these estimates across states

Estimates may not be comparable across states because of methodological changes discussed in “The method for calculating Adult Smoking has changed”.

Caution should be used when comparing these estimates across years

Estimates may not be comparable across years because of methodological changes discussed in “The method for calculating Adult Smoking has changed”.

Measure limitations

The BRFSS only surveys adults (age 18 and older), lacking data on adolescent smoking. The Youth Behavioral Risk Factor Survey attempts to fill this gap, but it currently does not provide enough data to estimate county-level smoking prevalence among youth. BRFSS also currently only asks about the use of cigarettes and not e-cigarettes which have grown in prominence. Additionally, new methods using biomarkers have shown that not all smokers are exposed to the same level of contaminants.4 The simple “current smoker” status question does not capture the thousands of chemical compounds in cigarettes and cigarette smoke nor take into account the effects of secondhand smoke.


The numerator is the number of adult respondents who reported “Yes” to the following question: Have you smoked at least 100 cigarettes in your entire life? and “Every day or some days” to the question: Do you now smoke cigarettes every day, some days, or not at all?


The denominator is the total number of adult respondents in a county.

Can This Measure Be Used to Track Progress

Modeled estimates have specific drawbacks with their usefulness in tracking progress in communities. Modeled data may not capture the effects of local conditions, such as health promotion policies. In order to better understand and validate modeled estimates, it can be helpful to supplement with additional local data.

Additionally, methodological changes limit the ability to track progress across years using this measure. For more information on methodological changes, see above.

Finding More Data

Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:

  • Age
  • Gender
  • Race
  • Education
  • Income
  • Subcounty Area

There are several methods to try to get more specific data than the county level. For larger counties, you can access county- or MSA-specific data from the CDC. However, using these data requires somewhat advanced analytic capabilities.

The PLACES Project provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Adult Smoking, across the United States.

In many states, you can access county-level BRFSS estimates, and in some cases, you can stratify those estimates by age, gender, income, education, or race.


1 U.S. Department of Health and Human Services. The health consequences of smoking—50 years of progress: A report of the Surgeon General. Atlanta: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014.

2 Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Sozial-und Praventivmedizin. 2001;46:S3-S42.

3 PLACES: Local data for better health. Centers for Disease Control and Prevention (CDC), Robert Wood Johnson Foundation, CDC Foundation. Accessed March 9, 2021.  

4 Delnevo CD, Bauer UE. Monitoring the tobacco use epidemic III: The host: data sources and methodological challenges. Preventive Medicine. 2009;48(suppl 1):S16-S23.

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