Behavioral Risk Factor Surveillance System Data Research Articles

Disparities in Preconception Health Indicators - Behavioral Risk Factor Surveillance System, 2013-2015, and Pregnancy Risk Assessment Monitoring System, 2013-2014.

Disparities in Preconception Health Indicators - Behavioral Risk Factor Surveillance System, 2013-2015, and Pregnancy Risk Assessment Monitoring System, 2013-2014.

Rural and Urban Differences in Passenger-Vehicle-Occupant Deaths and Seat Belt Use Among Adults - United States, 2014.

Problem/ConditionMotor-vehicle crashes are a leading cause of death in the United States. Compared with urban residents, rural residents are at an increased risk for death from crashes and are less likely to wear seat belts. These differences have not been well described by levels of rurality.Reporting Period2014.Description of SystemsData from the Fatality Analysis Reporting System (FARS) and the Behavioral Risk Factor Surveillance System (BRFSS) were used to identify passenger-vehicle–occupant deaths from motor-vehicle crashes and estimate the prevalence of seat belt use. FARS, a census of U.S. motor-vehicle crashes involving one or more deaths, was used to identify passenger-vehicle–occupant deaths among adults aged ≥18 years. Passenger-vehicle occupants were defined as persons driving or riding in passenger cars, light trucks, vans, or sport utility vehicles. Death rates per 100,000 population, age-adjusted to the 2000 U.S. standard population and the proportion of occupants who were unrestrained at the time of the fatal crash, were calculated. BRFSS, an annual, state-based, random-digit–dialed telephone survey of the noninstitutionalized U.S. civilian population aged ≥18 years, was used to estimate prevalence of seat belt use. FARS and BRFSS data were analyzed by a six-level rural-urban designation, based on the U.S. Department of Agriculture 2013 rural-urban continuum codes, and stratified by census region and type of state seat belt enforcement law (primary or secondary).ResultsWithin each census region, age-adjusted passenger-vehicle–occupant death rates per 100,000 population increased with increasing rurality, from the most urban to the most rural counties: South, 6.8 to 29.2; Midwest, 5.3 to 25.8; West, 3.9 to 40.0; and Northeast, 3.5 to 10.8. (For the Northeast, data for the most rural counties were not reported because of suppression criteria; comparison is for the most urban to the second-most rural counties.) Similarly, the proportion of occupants who were unrestrained at the time of the fatal crash increased as rurality increased. Self-reported seat belt use in the United States decreased with increasing rurality, ranging from 88.8% in the most urban counties to 74.7% in the most rural counties. Similar differences in age-adjusted death rates and seat belt use were observed in states with primary and secondary seat belt enforcement laws.InterpretationRurality was associated with higher age-adjusted passenger-vehicle–occupant death rates, a higher proportion of unrestrained passenger-vehicle–occupant deaths, and lower seat belt use among adults in all census regions and regardless of state seat belt enforcement type.Public Health ActionsSeat belt use decreases and age-adjusted passenger-vehicle–occupant death rates increase with increasing levels of rurality. Improving seat belt use remains a critical strategy to reduce crash-related deaths in the United States, especially in rural areas where seat belt use is lower and age-adjusted death rates are higher than in urban areas. States and communities can consider using evidence-based interventions to reduce rural-urban disparities in seat belt use and passenger-vehicle–occupant death rates.

Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas - Behavioral Risk Factor Surveillance System, United States, 2013 and 2014.

ProblemChronic diseases and conditions (e.g., heart diseases, stroke, arthritis, and diabetes) are the leading causes of morbidity and mortality in the United States. These conditions are costly to the U.S. economy, yet they are often preventable or controllable. Behavioral risk factors (e.g., excessive alcohol consumption, tobacco use, poor diet, frequent mental distress, and insufficient sleep) are linked to the leading causes of morbidity and mortality. Adopting positive health behaviors (e.g., staying physically active, quitting tobacco use, obtaining routine physical checkups, and checking blood pressure and cholesterol levels) can reduce morbidity and mortality from chronic diseases and conditions. Monitoring the health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services at multilevel public health points (states, territories, and metropolitan and micropolitan statistical areas [MMSA]) can provide important information for development and evaluation of health intervention programs.Reporting Period2013 and 2014.Description of the SystemThe Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit–dialed telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services and practices related to the leading causes of death and disability in the United States and participating territories. This is the first BRFSS report to include age-adjusted prevalence estimates. For 2013 and 2014, these age-adjusted prevalence estimates are presented for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico, Guam, and selected MMSA.ResultsAge-adjusted prevalence estimates of health status indicators, health care access and preventive practices, health risk behaviors, chronic diseases and conditions, and cardiovascular conditions vary by state, territory, and MMSA. Each set of proportions presented refers to the range of age-adjusted prevalence estimates of selected BRFSS measures as reported by survey respondents.The following are estimates for 2013. Adults reporting frequent mental distress: 7.7%–15.2% in states and territories and 6.3%–19.4% in MMSA. Adults with inadequate sleep: 27.6%–49.2% in states and territories and 26.5%–44.4% in MMSA. Adults aged 18–64 years having health care coverage: 66.9%–92.4% in states and territories and 60.5%–97.6% in MMSA. Adults identifying as current cigarette smokers: 10.1%–28.8% in states and territories and 6.1%–33.6% in MMSA. Adults reporting binge drinking during the past month: 10.5%–25.2% in states and territories and 7.2%–25.3% in MMSA. Adults with obesity: 21.0%–35.2% in states and territories and 12.1%–37.1% in MMSA. Adults aged ≥45 years with some form of arthritis: 30.6%–51.0% in states and territories and 27.6%–52.4% in MMSA. Adults aged ≥45 years who have had coronary heart disease: 7.4%–17.5% in states and territories and 6.2%–20.9% in MMSA. Adults aged ≥45 years who have had a stroke: 3.1%–7.5% in states and territories and 2.3%–9.4% in MMSA. Adults with high blood pressure: 25.2%–40.1% in states and territories and 22.2%–42.2% in MMSA. Adults with high blood cholesterol: 28.8%–38.4% in states and territories and 26.3%–39.6% in MMSA.The following are estimates for 2014. Adults reporting frequent physical distress: 7.8%–16.0% in states and territories and 6.2%–18.5% in MMSA. Women aged 21–65 years who had a Papanicolaou test during the past 3 years: 67.7%–87.8% in states and territories and 68.0%–94.3% in MMSA. Adults aged 50–75 years who received colorectal cancer screening on the basis of the 2008 U.S. Preventive Services Task Force recommendation: 42.8%–76.7% in states and territories and 49.1%–79.6% in MMSA. Adults with inadequate sleep: 28.4%–48.6% in states and territories and 25.4%–45.3% in MMSA. Adults reporting binge drinking during the past month: 10.7%–25.1% in states and territories and 6.7%–26.3% in MMSA. Adults aged ≥45 years who have had coronary heart disease: 8.0%–17.1% in states and territories and 7.6%–19.2% in MMSA. Adults aged ≥45 years with some form of arthritis: 31.2%–54.7% in states and territories and 28.4%–54.7% in MMSA. Adults with obesity: 21.0%–35.9% in states and territories and 19.7%–42.5% in MMSA.InterpretationPrevalence of certain chronic diseases and conditions, health risk behaviors, and use of preventive health services varies among states, territories, and MMSA. The findings of this report highlight the need for continued monitoring of health status, health care access, health behaviors, and chronic diseases and conditions at state and local levels.Public Health ActionState and local health departments and agencies can continue to use BRFSS data to identify populations at risk for certain unhealthy behaviors and chronic diseases and conditions. Data also can be used to design, monitor, and evaluate public health programs at state and local levels.

Use of Outpatient Cardiac Rehabilitation Among Heart Attack Survivors - 20 States and the District of Columbia, 2013 and Four States, 2015.

Heart disease is the leading cause of death in the United States (1). Each year, approximately 790,000 adults have a myocardial infarction (heart attack), including 210,000 that are recurrent heart attacks (2). Cardiac rehabilitation (rehab) includes exercise counseling and training, education for heart-healthy living, and counseling to reduce stress. Cardiac rehab provides patients with education regarding the causes of heart attacks and tools to initiate positive behavior change, and extends patients' medical management after a heart attack to prevent future negative sequelae (3). A systematic review has shown that after a heart attack, patients using cardiac rehab were 53% (95% confidence interval [CI]=41%-62%) less likely to die from any cause and 57% (95% CI=21%-77%) less likely to experience cardiac-related mortality than were those who did not use cardiac rehab (3). However, even with long-standing national recommendations encouraging use of cardiac rehab (4), the intervention has been underutilized. An analysis of 2005 Behavioral Risk Factor Surveillance System (BRFSS) data found that only 34.7% of adults who reported a history of a heart attack also reported subsequent use of cardiac rehab (5). To update these estimates, CDC used the most recent BRFSS data from 2013 and 2015 to assess the use of cardiac rehab among adults following a heart attack. Overall use of cardiac rehab was 33.7% in 20 states and the District of Columbia (DC) in 2013 and 35.5% in four states in 2015. Cardiac rehab use was underutilized overall and differences were evident by sex, age, race/ethnicity, level of education, cardiovascular risk status, and by state. Increasing use of cardiac rehab after a heart attack should be encouraged by health systems and supported by the public health community.

Trends in Cervical Cancer Screening Versus National Guidelines [23J

INTRODUCTION: Guidelines for cervical cancer screening via pap smear changed in 2012. Previous studies suggest mixed opinions about extended screening intervals. This descriptive study relates trends in pap smear use with the new recommendations. METHODS: Self-reported pap smear information from 2012-2014 Behavioral Risk Factor Surveillance System (BRFSS) surveys were weighted for the US population and subsequently analyzed using SPSS per The BRFSS Data User Guide. Age groups were determined by the 2012 cervical cancer screening guidelines. RESULTS: American women who reported “ever having a pap” declined 1.3% from 2012-2014. Specifically, Paps performed in 18-20 year olds decreased from 38.6% (n=2,580,856) to 27% (n=1,703,912). During this time period, the estimated percentage of women who “had a pap within the last year” declined from 53.6% (n=57,395,954) to 48.4% (n=49,965,054). In 2014 alone, approximately 70% (n=1,184,449) of 18-20 year olds and 23.6% (n=4,832,250) of women over 65 had their most recent pap within the previous year. Conversely, the number of women with a 3-year interval between Paps increased from 7.8% (n=8,327,940) to 9.5% (n=9,831,709) from 2012-2014. CONCLUSION: National Pap smear use is slowly aligning with 2012 guidelines, yet screening trends remain problematic. From 2012-2014, many women aged 18-20 or over 65 underwent a pap while no longer indicated, and few women utilized the extended interval. This can increase use of invasive follow-up procedures, anxiety, and cost, therefore contributing to excessive healthcare spending. Continued analysis of national pap testing will guide future studies that may unveil causes of excessive cervical cancer screening from the physician and patient perspective.

The Prevalence of Tobacco Use at Federally Qualified Health Centers in the United States, 2013.

We explored tobacco use across federally qualified health centers (FQHCs) and compared data on state-level tobacco use between FQHC patients and the general population. We used data from the Uniform Data System (UDS) and the Behavioral Risk Factor Surveillance System (BRFSS) to generate estimates of 2013 prevalence of tobacco use among adults aged 18 years or older. According to UDS data, the overall prevalence of tobacco use was 25.8% in FQHCs compared with 20.6% in the general population represented by BRFSS data, an average of 5.2 percentage points (range, −4.9 to 20.9) higher among FQHCs. Among FQHCs, the burden of tobacco use and the opportunity for offering cessation assistance is substantial.

Abstract P085: Cardiometabolic Syndrome Varies Significantly by Geography and Race/Ethnicity

Introduction: The relationship between cardiometabolic syndrome (CMS) and various risk factors has been studied in a variety of populations. Previously published research using 2007 Behavioral Risk Factor Surveillance System (BRFSS) data from Pennsylvania found that black and Hispanic adults had a significantly higher prevalence of CMS compared with whites. However, CMS has not been presented at the state level for the purpose of comparing the prevalence between states and between subpopulations within states. Hypothesis: This descriptive study examines the variation in prevalence of CMS by state and by race/ethnicity. We assessed the hypothesis that significant racial disparities exist in CMS prevalence between and within states. Methods: The sample size for this study included 2,380,047 noninstitutionalized adults aged 18 and older. A composite variable for CMS was created using BRFSS variables measuring obesity, hypercholesterolemia, angina (as a proxy for decreased high-density lipoprotein), prehypertension or hypertension, and prediabetes or diabetes. CMS was defined as having 3 or more of the 5 variables. Within BRFSS, obesity is defined as having a body mass index >=30kg/m*m based on self-reported height and weight. . Self-reported diagnosis of high cholesterol was used as a surrogate for elevated triglycerides. Self-reported diagnosis of angina or CHD was used as a surrogate for reduced HDL. Five years of BRFSS data (2011-2015) were aggregated to ensure adequate sample size for stratification by demographic variables. The survey weight used in the analysis was adjusted to represent the average population over the 5-year period. Both unadjusted and age-adjusted prevalence estimates were calculated. All statistical analyses were performed using STATA v14.1. Results: Unadjusted prevalence of cardiometabolic syndrome varied from 10.5% (95% CI: 10.0%-10.9%) of adults in Colorado to 23.1% (95% CI: 22.3%-24.0%) in Mississippi. Within Colorado, prevalence of CMS was 7.7% (95% CI: 7.2%-8.1%) among adults aged 18 to 64, and 21.7% (95% CI: 20.7%-22.7%) among adults aged 65 and older. In Mississippi prevalence of CMS was 19.8% (95% CI: 18.8%-20.7%) among adults aged 18 to 64 and 34.3% (95% CI: 32.8%-35.8%) among adults aged 65 and older. CMS prevalence is significantly higher among black adults compared with whites and Hispanics in almost all states (unadjusted for age). Age-adjusted comparisons between states are presented. Conclusions: Understanding the burden of CMS relative to other states and among select demographic groups within the state is vital to prevention efforts. Black adults are disproportionately afflicted with poor cardiometabolic health and the degree of affliction is dependent on state of residence. In conclusion, when it comes to CMS, it matters where you live, your age, and your racial/ethnic status.

Disparities in Cancer Survivorship Indicators in the Deep South Based on BRFSS Data: Recommendations for Survivorship Care Plans.

The purpose of this investigation was to describe cancer survivorship based on the Behavioral Risk Factor Surveillance System (BRFSS) cancer survivorship modules in Alabama, Georgia, and Mississippi, conducted in 2012 and 2014, and to investigate disparities across the US Deep South region. The optional BRFSS cancer survivorship module was introduced in 2009. Data from Alabama (2012), Georgia (2012), and Mississippi (2014) were assessed. Demographic factors were analyzed through weighted regression for risk of receiving cancer treatment summary information and follow-up care. Excluding nonmelanoma skin cancer cases, a total of 1105 adults in the Alabama 2012 survey, 571 adults in the Georgia 2012 survey, and 442 adults in the 2014 Mississippi survey reported ever having cancer and were available for analysis. Among Alabamians, those with a higher level of education (odds ratio [OR] 1.4, 95% confidence interval [CI] 1.1-1.7) and higher income (OR 1.3, 95% CI 1.1-1.6) were more likely to receive a written summary of their cancer treatments. Adults older than age 65 were only half as likely to receive a written summary of cancer treatments compared with adults 65 years or younger (OR 0.5, 95% CI 0.3-0.8). We found no significant differences in receipt of treatment summary by race or sex. Among those who reported receiving instructions from a doctor for follow-up care, these survivors tended to have a higher level of education, higher income, and were younger (younger than 65 years). Receipt of written or printed follow-up care was positively associated with higher income (OR 1.4, 95% CI 1.1-1.8) and inversely associated with age older than 65 years (OR 0.9, 95% CI 0.1-0.6) in Georgia. Addressing the gap identified between survivorship care plan development by the health team and the delivery of it to survivors is important given the evidence of disparities in the receipt of survivorship care plans across survivor age and socioeconomic status in the Deep South.

Surveillance for Health Care Access and Health Services Use, Adults Aged 18-64 Years - Behavioral Risk Factor Surveillance System, United States, 2014.

Surveillance for Health Care Access and Health Services Use, Adults Aged 18-64 Years - Behavioral Risk Factor Surveillance System, United States, 2014.

Health-Related Behaviors by Urban-Rural County Classification - United States, 2013.

Problem/ConditionPersons living in rural areas are recognized as a health disparity population because the prevalence of disease and rate of premature death are higher than for the overall population of the United States. Surveillance data about health-related behaviors are rarely reported by urban-rural status, which makes comparisons difficult among persons living in metropolitan and nonmetropolitan counties.Reporting Period2013.Description of SystemThe Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit-dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability. BRFSS data were analyzed for 398,208 adults aged ≥18 years to estimate the prevalence of five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations) by urban-rural status. For this report, rural is defined as the noncore counties described in the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties.ResultsApproximately one third of U.S. adults practice at least four of these five behaviors. Compared with adults living in the four types of metropolitan counties (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan), adults living in the two types of nonmetropolitan counties (micropolitan and noncore) did not differ in the prevalence of sufficient sleep; had higher prevalence of nondrinking or moderate drinking; and had lower prevalence of current nonsmoking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations. The overall age-adjusted prevalence of reporting at least four of the five health-related behaviors was 30.4%. The prevalence among the estimated 13.3 million adults living in noncore counties was lower (27.0%) than among those in micropolitan counties (28.8%), small metropolitan counties (29.5%), medium metropolitan counties (30.5%), large fringe metropolitan counties (30.2%), and large metropolitan centers (31.7%).InterpretationThis is the first report of the prevalence of these five health-related behaviors for the six urban-rural categories. Nonmetropolitan counties have lower prevalence of three and clustering of at least four health-related behaviors that are associated with the leading chronic disease causes of death. Prevalence of sufficient sleep was consistently low and did not differ by urban-rural status.Public Health ActionChronic disease prevention efforts focus on improving the communities, schools, worksites, and health systems in which persons live, learn, work, and play. Evidence-based strategies to improve health-related behaviors in the population of the United States can be used to reach the Healthy People 2020 objectives for these five self-reported health-related behaviors (sufficient sleep, current nonsmoking, nondrinking or moderate drinking, maintaining normal body weight, and meeting aerobic leisure time physical activity recommendations). These findings suggest an ongoing need to increase public awareness and public education, particularly in rural counties where prevalence of these health-related behaviors is lowest.

Abstract C62: Receipt of cervical cancer screening among women with access to care and other chronic diseases

Abstract Background and Objectives: Cervical cancer screening can save lives when abnormal cervical lesions and early cancers are detected and treated, but many women are not screened as recommended. Limited or no access to health care is a known barrier to screening; however, in 2012, among women with no cervical cancer screening in the previous 5 years, nearly 70% had health insurance and a regular doctor or health care provider. This study examined the factors related to why women aged 40-65 with access to health care are not being screened for cervical cancer as recommended. Methods: The 2014 Behavioral Risk Factor Surveillance System (BRFSS) survey was used to examine non-financial barriers to cervical cancer screening among women who reported insurance and a personal doctor or health care provider. Female respondents were asked if they had ever had a Pap test and if so, when this test was last performed. This study selected women who were aged 40-65 years, reported having medical insurance, at least one personal doctor or health care provider, no hysterectomy, or were not pregnant at the time of the survey. BRFSS data were weighted using advanced raking techniques. Chi-square testing was done to compare the characteristics of the respondents across screening history as screened versus never or rarely screened (more than 5 years). Results: Of the women analyzed for this study, 9.1% were never or rarely screened for cervical cancer and had access to health care with insurance and with a regular health care provider. When compared to women receiving timely screening, women who were never or rarely screened for cervical cancer had a higher prevalence of ever reporting a chronic disease, including chronic obstructive pulmonary disease (COPD) (6% vs 13.7%), arthritis (31.4% vs 38.1), depression (22% vs 31.4%), or diabetes (9.6% vs 15.4%) (all p < 0.01). Also, a significantly higher proportion of women who were never or rarely screened had more than 1 or 2 chronic diseases when compared to women screened on time (3-4 chronic diseases: 7.9% vs 16.6%, p=0.001). Women screened on time were more likely to have had no chronic disease (47.9%, p=0.001). Conclusion: When we examined factors related to cervical cancer screening among women who reported having health insurance and access to a regular doctor or healthcare provider, a larger proportion of women with multiple chronic diseases reported not receiving the recommended screening for cervical cancer. The results of this study underscore the importance of examining the relationship between having multiple chronic diseases and specific barriers to cervical cancer screening. Additional research is recommended to determine if physicians and other health care providers can effectively balance managing patients' chronic diseases and ensuring that patients receive recommended preventive services. Citation Format: Anatasha C. Crawford, Vicki Benard, Jessica King, Cheryll Thomas. Receipt of cervical cancer screening among women with access to care and other chronic diseases. [abstract]. In: Proceedings of the Ninth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2016 Sep 25-28; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(2 Suppl):Abstract nr C62.

Dental workforce availability and dental services utilization in Appalachia: a geospatial analysis.

There is considerable variation in dental services utilization across Appalachian counties, and a plausible explanation is that individuals in some geographical areas do not utilize dental care due to dental workforce shortage. We conducted an ecological study on dental workforce availability and dental services utilization in Appalachia. We derived county-level (n = 364) data on demographic and socioeconomic characteristics and dental services utilization in Appalachia from the 2010 Behavioral Risk Factor Surveillance System (BRFSS) using person-level data. We obtained county-level dental workforce availability and physician-to-population ratio estimates from Area Health Resources File and linked them to the county-level BRFSS data. The dependent variable was the proportion using dental services within the last year in each county (ranging from 16.6% to 91.0%). We described the association between dental workforce availability and dental services utilization using ordinary least squares regression and spatial regression techniques. Spatial analyses consisted of bivariate local indicators of spatial association (LISA) and geographically weighted regression (GWR). Bivariate LISA showed that counties in the central and southern Appalachian regions had significant (P < 0.05) low-low spatial clusters (low dental workforce availability, low percent dental services utilization). GWR revealed considerable local variations in the association between dental utilization and dental workforce availability. In the multivariate GWR models, 8.5% (t-statistics > 1.96) and 13.45% (t-statistics > 1.96) of counties showed positive and statistically significant relationships between the dental services utilization and workforce availability of dentists and dental hygienists, respectively. Dental workforce availability was associated with dental services utilization in the Appalachian region; however, this association was not statistically significant in all counties. The findings suggest that program and policy efforts to improve dental services utilization need to focus on factors other than increasing the dental workforce availability for many counties in Appalachia.

Trends in HIV Testing among Adults in Georgia: Analysis of the 2011-2015 BRFSS Data.

Georgia is ranked fifth highest among states for rates of human immunodeficiency virus (HIV) diagnosis. About 4% of persons living with HIV infection in the United States reside in Georgia, and almost 19% of these people do not know their HIV status. The present study examined the trends and associated factors of HIV testing among adults in Georgia between 2011 and 2015 by analyzing data of the Behavioral Risk Factor Surveillance System (BRFSS). A total of 31,094 persons aged ≥18 years were identified who responded to the question “Have you ever been tested for HIV?” Overall, there were 11,286 (44.2%) respondents who had been tested for HIV, compared to 19,808 (55.8%) who had not. There was a slight decrease in the percentage of respondents who have ever tested for HIV, from 45.6% in 2011 to 43.7% in 2015 (APC (annual percent change) = −0.98, not significant). Factors associated with HIV testing were being female (p = 0.004), black (p < 0.001), younger than 55 years (p < 0.001), single (p < 0.001), attaining education level above high school (p < 0.001), and earning annual income of $50,000 or less (p = 0.028). Overall in Georgia, there has been a slight decline in the temporal trend of HIV testing, and more than half of adults have never been tested for HIV. For reducing HIV transmission in Georgia, enhancing access and utilization of HIV testing should be a public health priority.

Age–period–cohort analyses of obesity prevalence in US adults

ObjectivesAge–period–cohort analysis is a stream of methodologies that decompose the temporal trends for disease risk into three time scales—age, calendar year (period) and year of birth (cohort). This study conducted age–period–cohort analyses of obesity prevalence in US adults. Study designRetrospective data analysis. MethodsWe constructed regression models based on anthropometric data from the 1999–2012 National Health and Nutrition Examination Survey to correct for the self-reported height/weight in the 1984–2014 Behavioral Risk Factor Surveillance System (BRFSS). We estimated fixed-effects age–period–cohort models based on the BRFSS data for the overall adult sample (n = 6,093,293) and by sex and race/ethnicity, adjusting for individual characteristics and the BRFSS survey design. ResultsAn inverted U-shaped age effect on obesity and a positive period effect characterized by over-time increase in obesity risk independent of age and cohort influences were identified in the overall sample and subgroups by sex and race/ethnicity. From 1984 to 2014, the adjusted obesity prevalence increased by 21.1 percentage points among US adults, and 20.9, 21.6, 21.0, 26.4 and 20.1 percentage points in men, women, non-Hispanic whites, African Americans and Hispanics, respectively. In contrast, no consistent evidence was found in support of the cohort effect—the adjusted obesity risk was comparable across birth cohorts after accounting for the age and period effects. ConclusionsShifts in the age distribution and nationwide secular changes may have fuelled the obesity epidemic in the USA over the past decades. Reversing the obesity epidemic may require understanding of the nationwide changes over time that affect weight gain across all population subgroups and promoting universal changes to diet, physical activity and the obesogenic environment.

Results and their implications from comparing respondents and proxy responses for non-respondents with cognitive difficulties on a telephone survey

BackgroundLimited study has been done on proxy responses for non-respondents with subjective cognitive decline (SCD) on the Behavioral Risk Factor Surveillance System (BRFSS). ObjectiveTo directly compare results for survey respondents with SCD with those for proxies provided for non-respondents with SCD. MethodsPublicly available 2011 BRFSS data from 120,485 households in 21 states were analyzed using Stata. Respondents ages 40 and older with SCD (n = 10,831) were compared with proxy responses for non-respondents ages 40 and older with SCD (n = 4296) living in households where the respondent did not have SCD. Outcome measures included functional difficulties associated with their SCD, needing help, receiving informal care, talking with a healthcare provider about their SCD, getting treatment, and having a dementia diagnosis. Logistic regression for each outcome controlled for age, household income, state of residence, and number of household adults. ResultsNon-respondents were significantly more likely than respondents by Pearson chi square tests with alpha = 0.05 to report all 6 outcomes. Adjusted odds ratios comparing non-respondents with respondents ranged from 2.61 (95% confidence interval: 2.22–3.07) for needing help, to 8.99 (6.60–12.24) for a dementia diagnosis and confirmed unadjusted results. ConclusionRespondent results only represent adults capable of answering a telephone survey. To represent all household adults and avoid nonresponse bias that may under-represent the true population parameters by as much as 70%, results must include both respondents and non-respondents. Other measures may be similarly affected if they inhibit one's ability to respond to a telephone survey (e.g. disability, stroke).

Diagnosed and Undiagnosed Diabetes Prevalence by County in the U.S., 1999-2012.

Previous analyses of diabetes prevalence in the U.S. have considered either only large geographic regions or only individuals in whom diabetes had been diagnosed. We estimated county-level trends in the prevalence of diagnosed, undiagnosed, and total diabetes as well as rates of diagnosis and effective treatment from 1999 to 2012. We used a two-stage modeling procedure. In the first stage, self-reported and biomarker data from the National Health and Nutrition Examination Survey (NHANES) were used to build models for predicting true diabetes status, which were applied to impute true diabetes status for respondents in the Behavioral Risk Factor Surveillance System (BRFSS). In the second stage, small area models were fit to imputed BRFSS data to derive county-level estimates of diagnosed, undiagnosed, and total diabetes prevalence, as well as rates of diabetes diagnosis and effective treatment. In 2012, total diabetes prevalence ranged from 8.8% to 26.4% among counties, whereas the proportion of the total number of cases that had been diagnosed ranged from 59.1% to 79.8%, and the proportion of successfully treated individuals ranged from 19.4% to 31.0%. Total diabetes prevalence increased in all counties between 1999 and 2012; however, the rate of increase varied widely. Over the same period, rates of diagnosis increased in all counties, while rates of effective treatment stagnated. Our findings demonstrate substantial disparities in diabetes prevalence, rates of diagnosis, and rates of effective treatment within the U.S. These findings should be used to target high-burden areas and select the right mix of public health strategies.

Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993-2009.

To explore the changing disparities in access to health care insurance in the United States using time-varying coefficient models. Secondary data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1993 to 2009 was used. A time-varying coefficient model was constructed using a binary outcome of no enrollment in health insurance plan versus enrolled. The independent variables included age, sex, education, income, work status, race, and number of health conditions. Smooth functions of odds ratios and time were used to produce odds ratio plots. Significant time-varying coefficients were found for all the independent variables with the odds ratio plots showing changing trends except for a constant line for the categories of male, student, and having three health conditions. Some categories showed decreasing disparities, such as the income categories. However, some categories had increasing disparities in health insurance enrollment such as the education and race categories. As the Affordable Care Act is being gradually implemented, studies are needed to provide baseline information about disparities in access to health insurance, in order to gauge any changes in health insurance access. The use of time-varying coefficient models with BRFSS data can be useful in accomplishing this task.

Surveillance for Certain Health Behaviors, Chronic Diseases, and Conditions, Access to Health Care, and Use of Preventive Health Services Among States and Selected Local Areas - Behavioral Risk Factor Surveillance System, United States, 2012.

Surveillance for Certain Health Behaviors, Chronic Diseases, and Conditions, Access to Health Care, and Use of Preventive Health Services Among States and Selected Local Areas - Behavioral Risk Factor Surveillance System, United States, 2012.

PD09-07 THE ROLE OF PATIENT RACE AND ETHNICITY IN PREDICTING PHYSICIAN RECOMMENDATION OF PROSTATE-SPECIFIC ANTIGEN (PSA) TESTING

You have accessJournal of UrologyProstate Cancer: Detection & Screening II1 Apr 2016PD09-07 THE ROLE OF PATIENT RACE AND ETHNICITY IN PREDICTING PHYSICIAN RECOMMENDATION OF PROSTATE-SPECIFIC ANTIGEN (PSA) TESTING George Turini, Annie Gjelsvik, Dragan Golijanin, Gyan Pareek, and Joseph Renzulli George TuriniGeorge Turini More articles by this author , Annie GjelsvikAnnie Gjelsvik More articles by this author , Dragan GolijaninDragan Golijanin More articles by this author , Gyan PareekGyan Pareek More articles by this author , and Joseph RenzulliJoseph Renzulli More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2902AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES A 2012 report by the US Preventive Service Task Force recommended against PSA-based screening among healthy men. African American men, however, have a higher incidence of, and mortality rate from, prostate cancer and may warrant more stringent screening. Uniform screening practices across races in accordance with the USPSTF statement may be detrimental the prognosis of men in high-risk groups. This study seeks to evaluate whether there was variability across categories of race in the proportion of men who were advised to have a PSA test by their physician. METHODS This cross-sectional study was performed using the 2012 Behavioral Risk Factor Surveillance System (BRFSS). Males under age 40 and females were excluded from the analysis. A subject's self-identified racial category as well as his response to whether or not he identified himself as Hispanic or Latino was used to operationalize “race”. Level of education, income, insurance status and number of reported primary physicians were included as confounders in our logistic regression analysis using STATA v13.4. RESULTS Among 134,262 men in the analytic sample with valid information about race and being advised to have a PSA checked, the majority (82%) identified themselves as “White”. Controlling for all other variables, “Black/African American” and “Hispanic” respondents had 1.51 (95% CI, 1.37-1.67) and 1.21 (95% CI, 1.08-1.35) the odds of being advised to have their PSA levels checked compared to “White” subjects, respectively, while “Asian” subjects only had 0.49 (95% CI, 0.40-0.60) the odds compared to “Whites”. CONCLUSIONS Among respondents to the 2012 BRFSS, “African American, Non-Hispanic” men were most likely to receive recommendation to have a PSA level checked compared to men of other races. This finding suggests that, in 2012, a population known to be at higher risk of developing and suffering morbidity/mortality from prostate cancer was more stringently counseled and screened than other populations of men without the same risk profile. Further investigation is warranted to elicit more specific reasons behind physician variation in offering PSA checks across difference races, ethnicities and demographic characteristics and follow-up analysis of 2014 BRFSS data available in the later part of 2015 will provide an interesting appraisal of any practice pattern changes that may have been implemented following the USPSTF policy statement release. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e236 Advertisement Copyright & Permissions© 2016MetricsAuthor Information George Turini More articles by this author Annie Gjelsvik More articles by this author Dragan Golijanin More articles by this author Gyan Pareek More articles by this author Joseph Renzulli More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Redrawing the US Obesity Landscape: Bias-Corrected Estimates of State-Specific Adult Obesity Prevalence.

BackgroundState-level estimates from the Centers for Disease Control and Prevention (CDC) underestimate the obesity epidemic because they use self-reported height and weight. We describe a novel bias-correction method and produce corrected state-level estimates of obesity and severe obesity.MethodsUsing non-parametric statistical matching, we adjusted self-reported data from the Behavioral Risk Factor Surveillance System (BRFSS) 2013 (n = 386,795) using measured data from the National Health and Nutrition Examination Survey (NHANES) (n = 16,924). We validated our national estimates against NHANES and estimated bias-corrected state-specific prevalence of obesity (BMI≥30) and severe obesity (BMI≥35). We compared these results with previous adjustment methods.ResultsCompared to NHANES, self-reported BRFSS data underestimated national prevalence of obesity by 16% (28.67% vs 34.01%), and severe obesity by 23% (11.03% vs 14.26%). Our method was not significantly different from NHANES for obesity or severe obesity, while previous methods underestimated both. Only four states had a corrected obesity prevalence below 30%, with four exceeding 40%–in contrast, most states were below 30% in CDC maps.ConclusionsTwelve million adults with obesity (including 6.7 million with severe obesity) were misclassified by CDC state-level estimates. Previous bias-correction methods also resulted in underestimates. Accurate state-level estimates are necessary to plan for resources to address the obesity epidemic.