Percentage of adults who are current smokers (age-adjusted). The 2023 County Health Rankings used data from 2020 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 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.
Data and methods
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 U.S. 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. For the County Health Rankings, data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators. HRQoL measures are age-adjusted to the 2000 U.S. standard population.
Prior to the 2016 County Health Rankings, 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 County Health Rankings 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. and can be found here: https://www.cdc.gov/500cities.
For the 2021 Rankings, 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. https://www.cdc.gov/places.
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 different age groups in the population. 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 County Health Rankings, the CDC’s BRFSS provided the Rankings with county-level estimates that were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Rankings, the CDC provided single-year modeled county-level estimates that included both landline and cell phone users. Beginning with the 2021 Rankings, the CDC has updated their modeling procedure for producing small-area estimates. Also in the 2021 Rankings, this measure is now age-adjusted. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community as possible.
Adult Smoking is created using statistical modeling
Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Modeling generates 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 their methods.
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
This measure could be used to measure progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. In the 2021 Rankings, this measure was age-adjusted and underwent additional methodological changes, further making comparisons with estimates from prior release years difficult. Finally, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress, especially in small geographic areas.
Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.
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:
- 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 at http://www.cdc.gov/brfss/smart/smart_data.htm. However, using this 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. You can find BRFSS resources for most states in our Data beyond the Rankings section.
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). Soz Praventivmed. 2001; 46:S3-S42.
3 PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021. https://www.cdc.gov/places.
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.