Behavioral Risk Factor Surveillance System

BackgroundIn recent years response rates on telephone surveys have been declining. Rates for the behavioral risk factor surveillance system (BRFSS) have also declined, prompting the use of new methods of weighting and the inclusion of cell phone sampling frames. A number of scholars and researchers have conducted studies of the reliability and validity of the BRFSS estimates in the context of these changes. As the BRFSS makes changes in its methods of sampling and weighting, a review of reliability and validity studies of the BRFSS is needed.MethodsIn order to assess the reliability and validity of prevalence estimates taken from the BRFSS, scholarship published from 2004-2011 dealing with tests of reliability and validity of BRFSS measures was compiled and presented by topics of health risk behavior. Assessments of the quality of each publication were undertaken using a categorical rubric. Higher rankings were achieved by authors who conducted reliability tests using repeated test/retest measures, or who conducted tests using multiple samples. A similar rubric was used to rank validity assessments. Validity tests which compared the BRFSS to physical measures were ranked higher than those comparing the BRFSS to other self-reported data. Literature which undertook more sophisticated statistical comparisons was also ranked higher.ResultsOverall findings indicated that BRFSS prevalence rates were comparable to other national surveys which rely on self-reports, although specific differences are noted for some categories of response. BRFSS prevalence rates were less similar to surveys which utilize physical measures in addition to self-reported data. There is very little research on reliability and validity for some health topics, but a great deal of information supporting the validity of the BRFSS data for others.ConclusionsLimitations of the examination of the BRFSS were due to question differences among surveys used as comparisons, as well as mode of data collection differences. As the BRFSS moves to incorporating cell phone data and changing weighting methods, a review of reliability and validity research indicated that past BRFSS landline only data were reliable and valid as measured against other surveys. New analyses and comparisons of BRFSS data which include the new methodologies and cell phone data will be needed to ascertain the impact of these changes on estimates in the future.

Health risk behaviors practiced during adolescence often persist into adulthood and contribute to the leading causes of morbidity and mortality in the United States. Youth health behavior data at the national, state, territorial, tribal, and local levels help monitor the effectiveness of public health interventions designed to promote adolescent health. The Youth Risk Behavior Surveillance System (YRBSS) is the largest public health surveillance system in the United States, monitoring a broad range of health-related behaviors among high school students. YRBSS includes a nationally representative Youth Risk Behavior Survey (YRBS) and separate state, local school district, territorial, and tribal school-based YRBSs. This overview report describes the surveillance system and the 2019 survey methodology, including sampling, data collection procedures, response rates, data processing, weighting, and analyses presented in this MMWR Supplement. A 2019 YRBS participation map, survey response rates, and student demographic characteristics are included. In 2019, a total of 78 YRBSs were administered to high school student populations across the United States (national and 44 states, 28 local school districts, three territories, and two tribal governments), the greatest number of participating sites with representative data since the surveillance system was established in 1991. The nine reports in this MMWR Supplement are based on national YRBS data collected during August 2018-June 2019. A full description of 2019 YRBS results and downloadable data are available (https://www.cdc.gov/healthyyouth/data/yrbs/index.htm).Efforts to improve YRBSS and related data are ongoing and include updating reliability testing for the national questionnaire, transitioning to electronic survey administration (e.g., pilot testing for a tablet platform), and exploring innovative analytic methods to stratify data by school-level socioeconomic status and geographic location. Stakeholders and public health practitioners can use YRBS data (comparable across national, state, tribal, territorial, and local jurisdictions) to estimate the prevalence of health-related behaviors among different student groups, identify student risk behaviors, monitor health behavior trends, guide public health interventions, and track progress toward national health objectives.

IntroductionRacial/ethnic disparities have been studied extensively. However, the combined influence of geographic location and economic status on specific health outcomes is less well studied. This study's objective was to examine 1) the disparity in chronic disease prevalence in the United States by county economic status and metropolitan classification and 2) the social gradient by economic status. The association of hypertension, arthritis, and poor health with county economic status was also explored.MethodsWe used 2013 Behavioral Risk Factor Surveillance System data. County economic status was categorized by using data on unemployment, poverty, and per capita market income. While controlling for sociodemographics and other covariates, we used multivariable logistic regression to evaluate the relationship between economic status and hypertension, arthritis, and self-rated health.ResultsPrevalence of hypertension, arthritis, and poor health in the poorest counties was 9%, 13%, and 15% higher, respectively, than in the most affluent counties. After we controlled for covariates, poor counties still had a higher prevalence of the studied conditions.ConclusionWe found that residents of poor counties had a higher prevalence of poor health outcomes than affluent counties, even after we controlled for known risk factors. Further, the prevalence of poor health outcomes decreased as county economics improved. Findings suggest that poor counties would benefit from targeted public health interventions, better access to health care services, and improved food and built environments.

OBJECTIVE: This study aims to explore the association between sleep duration and healthy aging in the older U.S. adults, utilizing a nationally representative sample. METHODS: Participants aged 65 and older from the 2016 Behavioral Risk Factor Surveillance System were eligible for this study. Healthy aging was defined as the absence of major chronic diseases, no physical functional limitations, and good subjective cognitive function and mental health. Logistic regression and restricted cubic spline curve analysis were employed to examine the potential association between sleep duration and healthy aging. Stratified analyses were conducted to examine the interactive effects of sleep duration with smoking, employment status, and other variables on healthy aging. RESULTS: 35,056 older adults (mean age 73.06 ± 5.31 years, 60.5% females) were included, among whom 5,782 (16.5%) achieved healthy aging. Sleep duration exhibited an inverted U-shaped relationship with healthy aging. Compared to those who slept for 7 h, the adjusted ORs (95% CI) of healthy aging for those who slept for ≤ 5 h, 6 h, 8 h, and ≥ 9 h were 0.51 (0.43, 0.59), 0.79 (0.72, 0.87), 0.96 (0.90, 1.03), and 0.65 (0.58, 0.73), respectively. Additionally, there was an interaction between sleep duration and employment & smoking status on healthy aging (P interaction = 0.003 & 0.009). Among the unemployed, the adjusted OR (95% CI) for healthy aging among those who slept ≤ 5 h was 0.41 (0.34, 0.49) [VS the employed: 0.75 (0.56, 1.01)]; among current and former smokers, the adjusted OR (95% CI) for healthy aging among those who slept ≤ 5 h was 0.37 (0.17, 0.82) and 0.39 (0.29, 0.51) [VS never smokers: 0.60 (0.49, 0.73)]. CONCLUSION: Both short and prolonged sleep duration may adversely impact healthy aging, especially among the unemployed and smokers. Health professionals should advise older adults that 7–8 h of sleep per day may promote overall health. Future research should explore the longitudinal relationship between sleep duration and healthy aging.