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Behavioral Risk Factor Surveillance System

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between the Centers for Disease Control and Prevention (CDC) and U.S. states and territories.
It is an ongoing telephone survey that collects state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services.
The BRFSS is the world's largest, on-going telephone health survery system, tracking health conditions and risk behaviors in the United States since 1984.
It provides crtical data to help guide public health action and promote healthy lifestyles.

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No research effort can result in a comprehensive disclosure of all relevant publications, especially on a publically available dataset which encompasses a wide range of topics. The articles presented here were obtained through an extensive search of publications indices (PubMed, ProQuest, and ScienceDirect). Within each search inquiry, keywords included “BRFSS,” “validity,” and/or “reliability.” Any article which included testing of BRFSS reliability and/or validity was included. Articles which expressed only opinions, without any comparisons or statistical testing were not considered. Given that the purpose of this research was to validate self-reported estimates in an era of declining landline telephone coverage, only those articles which have been published from 2004–2011 were included. Articles were then categorized and are presented in the following topic areas:
1. Access to health care/ general health
2. Immunization, preventive screening, and testing
3. Physical activity measures
4. Chronic disease
5. Mental health measures
6. Overweight and obesity measures
7. Tobacco and alcohol use measures
8. Responsible sexual behavior measures
9. Injury risk and violence
Quality of individual studies may vary significantly. Therefore a scoring rubric was devised to estimate the rigor of the tests of reliability and/or validity found in the literature. Higher rankings on the reliability rubric were achieved by authors who conducted reliability tests using repeated test/retest measures, used multiple samples/populations or multiple time periods. The rubric was also scored higher if authors conducted statistical tests, rather than simply comparing prevalence estimates. Authors who simply tested reliability by noting that results within the BRFSS were internally consistent were ranked lower on the reliability rubric. A similar rubric was used to rank validity assessments. Validity tests comparing the BRFSS to physical measures were ranked highest. Comparing BRFSS validity over time or comparing BRFSS against other self-reported data were ranked lower. Higher ranked assessments of validity and reliability were also characterized by more rigorous statistical comparisons, including the use of sensitivity and specificity measures [15 (link)], kappa and other statistics [16 (link)] or other statistical comparisons [17 (link)]. The rubric provided overall categorical rankings and is not intended to be interpreted as an interval measure of quality estimates. For each of the topics the following information is presented:
1. The number of articles relating to reliability of the BRFSS
2. The number of articles relating to validity of the BRFSS
3. The quality of reliability tests used by authors
4. The quality of validity tests
5. An overall assessment of the literature on reliability and validity of the BRFSS
Thus the method used to assess the literature followed the path illustrated in Figure 1.
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Publication 2013
Behavioral Risk Factor Surveillance System Injuries Mental Health Muscle Rigidity Obesity Physical Examination Population Group Tobacco Products Vaccination
Two independent and non-overlapping random digit dialing frames were used in this study with approximately 98 % coverage of all U.S. adult households [37 ]. To oversample smokers, both frames were stratified by household income and smoking rates at the county-level, where the poorest counties with the highest smoking rates were oversampled. Concordant with prior national tobacco survey studies [38 (link)], we oversampled cell phones numbers to maximize counts of young adults. To be considered eligible, a telephone number needed to reach a household with an English- or Spanish-speaking resident 18 years of age or older. Within the landline frame, if more than one eligible adult resided in the household, young adults and smokers were sampled at a higher rate than older adult nonsmokers.
The national survey was conducted between September 15, 2014 and May 31, 2015 and had an average completion time of 25 min. Calls were made Saturday through Thursday between 9 am and 9 pm (local time). Blaise CATI software [39 ] was used to both manage the sample and collect the data. No numbers were removed from calling until a minimum of 6 (cell phone) to 8 (landline) unsuccessful call attempts were made with at least one weekend, evening, and daytime call attempt. The sample resulted in 5,014 interviews and a weighted response rate (calculated using AAPOR Response Rate 4) of 42 %, a rate which is comparable to the 2012–2013 National Adult Tobacco Survey (44.9 %) [40 ] and the 2012 BRFSS (45.3 %) [41 ]. The remaining sample consisted of ineligible numbers (64,410), refusals from eligible households (2,623), or indeterminable eligibility status (41,877). All interviewers completed general and project-specific training before conducting the surveys and were monitored twice fortnightly. Informed consent for participation in the study was obtained verbally from respondents at the time of enrollment. The IRB at the University of North Carolina approved all study procedures and respondents were protected by a certificate of confidentiality.
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Publication 2016
Adult Aged Behavioral Risk Factor Surveillance System Eligibility Determination Fingers Hispanic or Latino Households Interviewers Non-Smokers Reading Frames Tobacco Products Young Adult
Data were collected as part of the 2014 Minnesota Adult Tobacco Survey, which uses a random digit dialing (RDD) methodology to obtain a cross-sectional sample of Minnesotan adults aged 18 years or older. Two sampling frames were used, one that included landline numbers and another that included cell phone numbers. Prescreening calls identified households and selected individuals within households; the main survey instrument was subsequently administered. A rigorous calling protocol was used, and letters were mailed to refusers and non-responders when addresses were available. Attempts were made to convert refusers. RDD response rates calculated by American Association for Public Opinion Research methodology were 25.2% for the landline sampling frame and 18.2% for the cell phone frame.23 Sampling weights were calculated based on sampling frame response rates and demographic characteristics known to be correlated with tobacco use behaviours, to obtain unbiased population level estimates. More methodological detail is available at http://www.mnadulttobaccosurvey.org. The final sample in 2014 included 9304 participants; 9301 of the participants provided valid responses for the items considered in this analysis.
Smoking status was established according to the historically common Behavioural Risk Factor Surveillance System (BRFSS) methodology. Current smokers had smoked ≥100 cigarettes in their lifetime and now smoked ‘every day’ or ‘some days’; former smokers had smoked ≥100 cigarettes in their lifetime, but now smoked ‘not at all’; and never smokers had not smoked ≥100 cigarettes in their lifetime. E-cigarette use was measured by two items. Participants were first asked, “Have you ever used an electronic cigarette, even just one time in your entire life?” Affirmative answers were followed by the question, “During the past 30 days, on how many days did you use e-cigarettes?” Responses were entered as integers by the data collector; respondents offering non-integer responses were prompted to provide an integer. Respondents who had ever used e-cigarettes were asked whether each of the following was a reason for use: to quit other tobacco products; to cut down on other tobacco products because they are affordable; because they are available in menthol flavour; because they are available in flavours other than menthol; to use them in places where other tobacco products are not allowed; curiosity about e-cigarettes; and because you believe these might be less harmful than other tobacco products. Based on the findings of Pepper et al16 (link), reasons were classified as goal oriented or non-goal oriented.
All analyses were conducted with the R software package, V.3.1.1, using the survey package V.3.30-3. All population estimates are presented with 95% CIs. Where direct comparisons of CIs are insufficient to establish significance at the α=0.05 level (ie, where CIs overlapped), we report p values for pairwise comparisons that were calculated using linear regression.
Publication 2015
Adult Behavioral Risk Factor Surveillance System Fingers Flavor Enhancers Households Menthol Muscle Rigidity Piper nigrum Reading Frames Tobacco Products

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Publication 2013
Adult Behavioral Risk Factor Surveillance System Diagnosis Index, Body Mass Patients Pharmaceutical Preparations
We applied previously described small area models to estimate the prevalence of cigarette smoking for US counties [21 (link)-23 (link)]. In brief, we constructed a family of logistic hierarchical mixed effects regression models for each outcome, stratified by sex. These models incorporate spatial and temporal smoothing and a series of county- and state-level covariates to improve predictions for all counties, including those with limited data available in a given year from the BRFSS. More details on the regression models and the county- and state-level data sources incorporated in the models can be found in Additional files 1 and 2. These models allowed us to generate annual estimates of total and daily cigarette smoking prevalence for male and female adults (age 18 and older) in all US counties and county equivalents. All estimates were age-standardized following the age structure of the 2000 census [24 ]. The uncertainty of the prevalence estimates was assessed using simulation methods [25 (link)].
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Publication 2014
Adult Behavioral Risk Factor Surveillance System Females Males

Most recents protocols related to «Behavioral Risk Factor Surveillance System»

Information was collected on participants’ gender identification, sexual orientation, perceived household wealth, and language spoken most at home. Participants were also asked to indicate whether they had ever been diagnosed with a range of mental illnesses (e.g., major depression, social anxiety disorder, generalised anxiety disorder, panic disorder) by a professional, and to complete the Adapted Behavioural Risk Factor Surveillance System Adverse Childhood Experience Module (BRFSS-ACE). The BRFSS-ACE (e.g., Have you ever felt like your life was in serious danger or that you would be harmed?; Have you ever been in out-of-home or foster care?) is a widely used 8-item scale to assess adverse childhood experiences and has acceptable psychometric properties [38 (link)]. For both mental illness history and adverse childhood experiences, we calculated a composite score from item responses, which was then dichotomised to identify participants who had no mental illness history/adverse experiences and those who had a mental illness history/adverse experiences.
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Publication 2023
Anxiety Disorders Behavioral Risk Factor Surveillance System Feelings Gender Households Major Depressive Disorder Mental Disorders Panic Disorder Phobia, Social Psychometrics Sexual Orientation
We measured race consciousness using a question from the Behavioral Risk Factor Surveillance System, Reactions to Race optional module. Respondents were asked, “How often do you think about your race?” Response options included never, once a year, once a month, once a week, once a day, once an hour, and constantly.
Publication 2023
Behavioral Risk Factor Surveillance System Consciousness
We measured healthcare coverage using a question from the Behavioral Risk Factor Surveillance System, Core Section on healthcare access. Respondents were asked, “Do you have any kind of healthcare coverage, including health insurance, prepaid plans, such as HMOS, government plans, such as Medicare, or Indian Health Service?” Response options included yes and no.
Publication 2023
Behavioral Risk Factor Surveillance System Health Insurance Infantile Neuroaxonal Dystrophy
We measured racial misclassification using two items—the socially assigned race question from the Behavioral Risk Factor Surveillance System, Reactions to Race optional module and self-identified race [39 ]. After having reported their race and ethnicity, respondents were later given this prompt: “Earlier I asked you to self-identify your race. Now I will ask you how other people identify you and treat you. How do other people usually classify you in this country?” They could respond that they were Asian, Black, Hispanic/Latinx, American Indian or Alaska Native, NHPI, White, or some other group. Because the entire analytical sample was selected based on a self-identified race of NHPI, any respondents in this subsample who indicated their socially assigned race was also NHPI were coded as experiencing a match between their self-identified and socially assigned race. Those who selected they were perceived to be any other racial group were coded as experiencing racial misclassification.
Publication 2023
Alaskan Natives American Indians Asian Persons Behavioral Risk Factor Surveillance System Ethnicity Hispanics Latinx Racial Groups
We followed the Strengthening the Reporting of Observational Studies in Epidemiology Guidelines for cross-sectional studies in the reporting of this study. This study was approved by the institutional review board of Brigham and Women’s Hospital. Data were obtained from the Behavioral Risk Factor Surveillance System (BRFSS) database from 2012, 2014, 2016, 2018, and 2020. BRFSS is the largest longitudinal national survey system of the United States maintained by the Center for Disease Control and Prevention for health-related risk behaviors, chronic health conditions, and the use of preventive services. The database is based on annual surveys conducted via telephone calls to create a stratified random representative sample of adult residents in the United States. Patients were weighted by age, sex, race and ethnicity, educational level, marital status, property ownership, and telephone ownership. The median weighted response rates during the study period were 45.2%, 47.9%, 47.0%, 49.9%, and 47.9%, respectively, for each year of data included.
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Publication 2023
Adult Behavioral Risk Factor Surveillance System Chronic Condition Ethics Committees, Research Ethnicity Patients Woman

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More about "Behavioral Risk Factor Surveillance System"

The Behavioral Risk Factor Surveillance System (BRFSS) is a comprehensive, nationwide health survey conducted by the Centers for Disease Control and Prevention (CDC) in collaboration with U.S. states and territories.
This ongoing telephone-based survey collects valuable data on health-related behaviors, chronic conditions, and preventive services utilization among American residents.
As the world's largest continuous telephone health survey, BRFSS has been tracking crucial health indicators in the United States since 1984.
This rich dataset provides essential insights to guide public health interventions and promote healthier lifestyles.
BRFSS data can be analyzed using a variety of statistical software packages, including SAS (versions 9.4 and above), Stata (versions 14, 15, and 16), and Cobas Integra.
The BRFSS survey covers a wide range of topics, such as physical activity, nutrition, tobacco use, alcohol consumption, preventive screenings, and chronic diseases like diabetes, hypertension, and obesity.
By leveraging this comprehensive dataset, researchers and public health professionals can uncover valuable trends, identify high-risk populations, and develop targeted strategies to address pressing health concerns.
Optimizing the use of BRFSS data is crucial for enhancing the reproducibility and accuracy of public health research.
Tools like PubCompare.ai can streamline the research process by automating the identification of the best protocols and products from literature, preprints, and patents.
This AI-powered approach helps researchers effortlessly locate and utilize the most relevant BRFSS-related resources, ultimately elevating the quality and impact of their findings.
Whether you're a researcher, a public health practitioner, or simply interested in understanding the health landscape of the United States, the Behavioral Risk Factor Surveillance System (BRFSS) is an invaluable resource that provides a wealth of insights to improve population health and well-being.