> For the complete documentation index, see [llms.txt](https://docs.bcbi.brown.edu/codiac-for-health/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.bcbi.brown.edu/codiac-for-health/ursa-ri/datasets/behavioral-risk-factor-surveillance-system-brfss.md). # Behavioral Risk Factor Surveillance System (BRFSS) ## Overview The Behavioral Risk Factor Surveillance System (BRFSS) was established in 1984 by the U.S. Centers for Disease Control and Prevention (CDC) to collect prevalence data regarding risk behaviors and preventive health practices among adult residents across all 50 states, the District of Columbia, and participating U.S. territories. The state-based, cross-sectional telephone survey was conducted monthly by state health departments. The survey has consisted of standard core questions on emerging health issues, rotating core questions asked every other year, optional modules, and state-added questions on health priorities specific to the respective state. Collected data was processed through editing, weighting, and analysis to provide critical information on health status. The BRFSS aims to inform the management of health-related policies and priorities at both the state and national levels. Published data, reports, questionnaires, and analysis tools are publicly accessible through the CDC BRFSS website. Data files are provided in ASCII (fixed record length) and SAS Transport (.XTP) formats. ## Methodology and Generation The BRFSS dataset was created using a standardized questionnaire developed in collaboration between the CDC and U.S. state health departments. The survey consisted of a standard core, rotating core, and optional modules. Data was collected by states or private contractors following CDC protocols, which included multiple call attempts and specific schedules to ensure representative participation on weeknights and weekends. The sampling design utilized disproportionate stratified sampling (DSS) for landlines to increase the efficiency of finding residential numbers and a randomly generated frame for cellular phones. The collected data underwent design weighting–which adjusts for the number of adults and telephones in a household–followed by iterative proportional fitting–which aligns the sample with population demographics like age, sex, race, education, and phone ownership to ensure the final results are representative of the population. ## Standardized Healthcare Vocabularies ### Standard and Core Questions Standardized queries asked by all states either annually or every other year to ensure consistent national data. ### Calculated Variables Derived clinical and behavioral metrics, such as Body Mass Index (BMI), which are identifiable in the dataset by a preceding underscore (ie. \_BMI). ### Variable Weights Specific internal codes including final weight for combined landline/cellular data (\_LLCPWT) and stratum weight (\_STRWT) used for complex sampling analysis. ### Complex Sampling Identifiers Standardized variables for stratification (\_STSTR) and clustering (\_PSU) required for proper analysis in statistical software. ### AAPOR Disposition Codes Standardized result codes (ie. completed interview, refusal, ineligible) established by the American Association of Public Opinion Research (AAPOR) to calculate response and cooperation rates. ### Demographic Labels Standardized categories for reporting age, race, ethnicity, marital status, education level, and home ownership. ## References 1. U.S. Centers for Disease Control and Prevention. [The BRFSS Data User Guide June 2013](https://www.cdc.gov/brfss/data_documentation/pdf/UserguideJune2013.pdf) \[Internet]. Aug 15, 2013. 2. U.S. Centers for Disease Control and Prevention. [BRFSS Overview 2024](https://www.cdc.gov/brfss/annual_data/2024/pdf/Overview_2024-508.pdf) \[Internet]. 2025. 3. U.S. Centers for Disease Control and Prevention. [2024 BRFSS Survey Data and Documentation](https://www.cdc.gov/brfss/annual_data/annual_2024.html) \[Internet]. 2025. ## Resources ### Articles * Gundersen DA, ZuWallack RS, Dayton J, Echeverría SE, Delnevo CD. [Assessing the Feasibility and Sample Quality of a National Random-digit Dialing Cellular Phone Survey of Young Adults](https://pmc.ncbi.nlm.nih.gov/articles/PMC3864711/). Am J Epidemiol. 2014 Jan 1;179(1):39–47. doi:10.1093/aje/kwt226 PubMed PMID: 24100957; PubMed Central PMCID: PMC3864711. * Hsia J, Zhao G, Town M, Ren J, Okoro CA, Pierannunzi C, et al. [Comparisons of Estimates From the Behavioral Risk Factor Surveillance System and Other National Health Surveys, 2011−2016](https://www.ajpmonline.org/article/S0749-3797\(20\)30091-X/fulltext). American Journal of Preventive Medicine. 2020 Jun 1;58(6):e181–90. doi:10.1016/j.amepre.2020.01.025 PubMed PMID: 32444008. * Hu SS, Pierannunzi C, Balluz L. [Integrating a Multimode Design Into a National Random-Digit–Dialed Telephone Survey](https://pmc.ncbi.nlm.nih.gov/articles/PMC3221584/). Prev Chronic Dis. 2011 Oct 15;8(6):A145. PubMed PMID: 22005638; PubMed Central PMCID: PMC3221584. * Li C, Balluz LS, Ford ES, Okoro CA, Zhao G, Pierannunzi C. [A comparison of prevalence estimates for selected health indicators and chronic diseases or conditions from the Behavioral Risk Factor Surveillance System, the National Health Interview Survey, and the National Health and Nutrition Examination Survey, 2007–2008](https://www.sciencedirect.com/science/article/pii/S0091743512001090). Preventive Medicine. 2012 Jun 1;54(6):381–7. doi:10.1016/j.ypmed.2012.04.003 * Pierannunzi C, Hu SS, Balluz L. [A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011](https://link.springer.com/article/10.1186/1471-2288-13-49). BMC Med Res Methodol. 2013 Mar 24;13(1):49. doi:10.1186/1471-2288-13-49 ### Links * [CDC BRFSS Survey Data & Documentation](https://www.cdc.gov/brfss/data_documentation/index.htm) * [CDC BRFSS Questionnaires](https://www.cdc.gov/brfss/questionnaires/index.htm) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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