Racial and Ethnic Differences in Social Determinants of Health and Health-Related Social Needs Among Adults - Behavioral Risk Factor Surveillance System, United States, 2022.

Social determinants of health (SDOH) are a broad array of social and contextual conditions where persons are born, live, learn, work, play, worship, and age that influence their physical and mental wellbeing and quality of life. Using 2022 Behavioral Risk Factor Surveillance System data, this study assessed measures of adverse SDOH and health-related social needs (HRSN) among U.S. adult populations. Measures included life satisfaction, social and emotional support, social isolation or loneliness, employment stability, food stability/security, housing stability/security, utility stability/security, transportation access, mental well-being, and health care access. Prevalence ratios were adjusted for age, sex, education, marital status, income, and self-rated health. Social isolation or loneliness (31.9%) and lack of social and emotional support (24.8%) were the most commonly reported measures, both of which were more prevalent among non-Hispanic (NH) American Indian or Alaska Native, NH Black or African American, NH Native Hawaiian or other Pacific Islander, NH multiracial, and Hispanic or Latino adults than among NH White adults. The majority of prevalence estimates for other adverse SDOH and HRSN were also higher across all other racial and ethnic groups (except for NH Asian) compared with NH White adults. SDOH and HRSN data can be used to monitor needed social and health resources in the U.S. population and help evaluate population-scale interventions.

Vital Signs: Mammography Use and Association with Social Determinants of Health and Health-Related Social Needs Among Women - United States, 2022.

Introduction: Approximately 40,000 U.S. women die from breast cancer each year. Mammography is recommended to screen for breast cancer and reduce breast cancer mortality. Adverse social determinants of heath (SDOH) and health-related social needs (HRSNs) (e.g., lack of transportation and social isolation) can be barriers to getting mammograms. Methods: Data from the 2022 Behavioral Risk Factor Surveillance System were analyzed to estimate the prevalence of mammography use within the previous 2 years among women aged 40-74 years by jurisdiction, age group, and sociodemographic factors. The association between mammography use and measures of SDOH and HRSNs was assessed for jurisdictions that administered the Social Determinants and Health Equity module. Results: Among women aged 50-74 years, state-level mammography use ranged from 64.0% to 85.5%. Having health insurance and a personal health care provider were associated with having had a mammogram within the previous 2 years. Among women aged 50-74 years, mammography prevalence was 83.2% for those with no adverse SDOH and HRSNs and 65.7% for those with three or more adverse SDOH and HRSNs. Life dissatisfaction, feeling socially isolated, experiencing lost or reduced hours of employment, receiving food stamps, lacking reliable transportation, and reporting cost as a barrier for access to care were all strongly associated with not having had a mammogram within the previous 2 years. Conclusions and Implications for Public Health Practice: Identifying specific adverse SDOH and HRSNs that women experience and coordinating activities among health care providers, social services, community organizations, and public health programs to provide services that help address these needs might increase mammography use and ultimately decrease breast cancer deaths.

National, State-Level, and County-Level Prevalence Estimates of Adults Aged ≥18 Years Self-Reporting a Lifetime Diagnosis of Depression - United States, 2020.

Depression is a major contributor to mortality, morbidity, disability, and economic costs in the United States (1). Examining the geographic distribution of depression at the state and county levels can help guide state- and local-level efforts to prevent, treat, and manage depression. CDC analyzed 2020 Behavioral Risk Factor Surveillance System (BRFSS) data to estimate the national, state-level, and county-level prevalence of U.S. adults aged ≥18 years self-reporting a lifetime diagnosis of depression (referred to as depression). During 2020, the age-standardized prevalence of depression among adults was 18.5%. Among states, the age-standardized prevalence of depression ranged from 12.7% to 27.5% (median = 19.9%); most of the states with the highest prevalence were in the Appalachian* and southern Mississippi Valley† regions. Among 3,143 counties, the model-based age-standardized prevalence of depression ranged from 10.7% to 31.9% (median = 21.8%); most of the counties with the highest prevalence were in the Appalachian region, the southern Mississippi Valley region, and Missouri, Oklahoma, and Washington. These data can help decision-makers prioritize health planning and interventions in areas with the largest gaps or inequities, which could include implementation of evidence-based interventions and practices such as those recommended by The Guide to Community Preventive Services Task Force (CPSTF) and the Substance Abuse and Mental Health Services Administration (SAMHSA).

Chronic Conditions Among Adults Aged 18─34 Years - United States, 2019.

Chronic conditions are common, costly, and major causes of death and disability.* Addressing chronic conditions and their determinants in young adulthood can help slow disease progression and improve well-being across the life course (1); however, recent prevalence estimates examining chronic conditions in young adults overall and by subgroup have not been reported. CDC analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) to measure prevalence of 11 chronic conditions among adults aged 18-34 years overall and by selected characteristics, and to measure prevalence of health-related risk behaviors by chronic condition status. In 2019, more than one half (53.8%) of adults aged 18-34 years reported having at least one chronic condition, and nearly one quarter (22.3%) reported having more than one chronic condition. The most prevalent conditions were obesity (25.5%), depression (21.3%), and high blood pressure (10.7%). Differences in the prevalence of having a chronic condition were most noticeable between young adults with a disability (75.8%) and without a disability (48.3%) and those who were unemployed (62.3%) and students (45.8%). Adults aged 18-34 years with a chronic condition were more likely than those without one to report binge drinking, smoking, or physical inactivity. Coordinated efforts by public and private sectors might help raise awareness of chronic conditions among young adults and help improve the availability of evidence-based interventions, policies, and programs that are effective in preventing, treating, and managing chronic conditions among young adults (1).

Health-Related Behavioral Risk Factors and Obesity Among American Indians and Alaska Natives of the United States: Assessing Variations by Indian Health Service Region.

INTRODUCTION: Health-related behavioral risk factors and obesity are linked to high risk for multiple chronic diseases. We examined the prevalence of these risk factors among American Indians and Alaska Natives (AI/ANs) compared with that of non-Hispanic Whites and across Indian Health Service (IHS) regions. METHODS: We used 2017 Behavioral Risk Factor Surveillance System data from participants in 50 states and the District of Columbia to assess 4 behavioral risk factors (current cigarette smoking, heavy drinking, binge drinking, and physical inactivity) and obesity. We analyzed disparities in these risk factors between AI/AN and non-Hispanic White participants, nationwide and by IHS region, by conducting log-linear regression analyses while controlling for potential confounders. RESULTS: Nationwide, crude prevalence of current smoking, physical inactivity, and obesity were significantly higher among AI/AN than non-Hispanic White participants. After adjustment for sociodemographic characteristics, AI/AN participants were 11% more likely to report current smoking (P < .05) and 23% more likely to report obesity (P < .001) than non-Hispanic White participants. These patterns persisted in most IHS regions with some exceptions. In the Southwest region, AI/AN participants were 39% less likely to report current smoking than non-Hispanic White participants (P < .001). In the Pacific Coast region, compared with non-Hispanic White participants, AI/AN participants were 54% less likely to report heavy drinking (P < .01) but 34% more likely to report physical inactivity (P < .05). Across IHS regions, AI/AN participants residing in Alaska and the Northern Plains regions had the highest prevalence of current smoking and binge drinking, and those in the Southwest and Pacific Coast regions had the lowest prevalence of current smoking. AI/AN participants in the Southwest region had the lowest prevalence of physical inactivity, and those in the Southern Plains region had the highest prevalence of obesity. CONCLUSIONS: The findings of this study support the importance of public health efforts to address and improve behavioral risk factors related to chronic disease in AI/AN people, both nationwide and among IHS regions, through culturally appropriate interventions.

Risk Factors for Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System.

BACKGROUND: Data on risk factors for coronavirus disease 2019 (COVID-19)-associated hospitalization are needed to guide prevention efforts and clinical care. We sought to identify factors independently associated with COVID-19-associated hospitalizations. METHODS: Community-dwelling adults (aged ≥18 years) in the United States hospitalized with laboratory-confirmed COVID-19 during 1 March-23 June 2020 were identified from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), a multistate surveillance system. To calculate hospitalization rates by age, sex, and race/ethnicity strata, COVID-NET data served as the numerator and Behavioral Risk Factor Surveillance System estimates served as the population denominator for characteristics of interest. Underlying medical conditions examined included hypertension, coronary artery disease, history of stroke, diabetes, obesity, severe obesity, chronic kidney disease, asthma, and chronic obstructive pulmonary disease. Generalized Poisson regression models were used to calculate adjusted rate ratios (aRRs) for hospitalization. RESULTS: Among 5416 adults, hospitalization rates (all reported as aRR [95% confidence interval]) were higher among those with ≥3 underlying conditions (vs without) (5.0 [3.9-6.3]), severe obesity (4.4 [3.4-5.7]), chronic kidney disease (4.0 [3.0-5.2]), diabetes (3.2 [2.5-4.1]), obesity (2.9 [2.3-3.5]), hypertension (2.8 [2.3-3.4]), and asthma (1.4 [1.1-1.7]), after adjusting for age, sex, and race/ethnicity. Adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults aged ≥65 or 45-64 years (vs 18-44 years), males (vs females), and non-Hispanic black and other race/ethnicities (vs non-Hispanic whites). CONCLUSIONS: Our findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions.

Health-related behaviors and health insurance status among US adults: Findings from the 2017 behavioral risk factor surveillance system.

Health insurance coverage has increased overtime in the US. This study examined the associations between health insurance status and adoption of health-related behaviors among US adults. Using data collected through the 2017 Behavioral Risk Factor Surveillance System on health insurance coverage and type of insurance, we examined four health-related behaviors (i.e., no tobacco use, nondrinking or moderate drinking, meeting aerobic physical activity recommendations, and having a healthy body weight) and their associations with health insurance status. We conducted log-linear regression analyses to assess the associations with adjustment for potential confounders. Results showed the percentages of adults who reported no tobacco use or meeting physical activity recommendations were significantly higher, and the percentages of adults with a healthy body weight were significantly lower among those who were insured versus uninsured, or among adults with private insurance versus uninsured. Adults with health insurance also had a higher prevalence of reporting all 4 health-related behaviors than those uninsured. These patterns persisted after multivariable adjustment for potential confounders including sociodemographics, routine checkup, and number of chronic diseases. Adults with public insurance were 7% more likely to report no tobacco use than adults who were uninsured. Additionally, adults with private insurance were 8% and 7% more likely to report no tobacco use and meeting physical activity recommendations, respectively, but 10% less likely to report nondrinking or moderate drinking than adults with public insurance. In conclusion, we found significant associations existed between having health insurance coverage and engaging in some health-related behaviors among US adults.

Estimating Undercoverage Bias of Internet Users.

INTRODUCTION: In the last decade, response rates to the Behavioral Risk Factor Surveillance System (BRFSS) surveys have been declining. Attention has turned to the possibility of using web surveys to complement or replace BRFSS, but web surveys can introduce coverage bias as a result of excluding noninternet users. The objective of this study was to describe undercoverage bias of internet use. METHODS: We used data from 402,578 respondents who completed BRFSS questions in 2017 on internet use, self-reported health, current smoking, and binge drinking. We examined undercoverage bias of internet use by partitioning it into a product of 2 components: proportion of noninternet use and difference in the prevalences of interest (self-reported health, current smoking, and binge drinking) between internet users and noninternet users. RESULTS: Overall, the weighted proportion of noninternet use overall was 15.0%; the proportion increased with an increase in age and a decrease in education and, by race/ethnicity, was lowest among non-Hispanic white respondents. The overall relative bias was -19.2% for self-reported health, -4.0% for current cigarette smoking, and 8.4% for binge drinking. For all 3 variables of interest, we found large biases and relative biases in some demographic subgroups. CONCLUSION: Undercoverage bias of internet use existed in the 3 studied variables. Both proportion of noninternet users and difference in prevalences of studied variables between internet users and noninternet users contributed to the bias to different degrees. These findings have implications on helping health-related behavioral risk factor surveys transition to more cost-effective survey modes than telephone only.

Small Area Estimation of Cancer Risk Factors and Screening Behaviors in US Counties by Combining Two Large National Health Surveys.

BACKGROUND: National health surveys, such as the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS), collect data on cancer screening and smoking-related measures in the US noninstitutionalized population. These surveys are designed to produce reliable estimates at the national and state levels. However, county-level data are often needed for cancer surveillance and related research. METHODS: To use the large sample sizes of BRFSS and the high response rates and better coverage of NHIS, we applied multilevel models that combined information from both surveys. We also used relevant sources such as census and administrative records. By using these methods, we generated estimates for several cancer risk factors and screening behaviors that are more precise than design-based estimates. RESULTS: We produced reliable, modeled estimates for 11 outcomes related to smoking and to screening for female breast cancer, cervical cancer, and colorectal cancer. The estimates were produced for 3,112 counties in the United States for the data period from 2008 through 2010. CONCLUSION: The modeled estimates corrected for potential noncoverage bias and nonresponse bias in the BRFSS and reduced the variability in NHIS estimates that is attributable to small sample size. The small area estimates produced in this study can serve as a useful resource to the cancer surveillance community.

Prevalence of Five Health-Related Behaviors for Chronic Disease Prevention Among Sexual and Gender Minority Adults - 25 U.S. States and Guam, 2016.

In recent decades, public health awareness of health disparities among lesbian, gay, bisexual, and transgender (LGBT) populations has increased (1). Healthy People 2020 included objectives to improve health of LGBT persons.† Five key health-related behaviors were found to be likely associated with reduced all-cause mortality: never smoking, performing regular physical activity, consuming no or moderate amounts of alcohol, having a normal body weight, and obtaining sufficient sleep daily (2). CDC estimated these five health-related behaviors among adults aged ≥21 years by sexual orientation and transgender status using data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) in 25 U.S. states and Guam. Patterns of these five health-related behaviors varied by sexual orientation among men and women, and among transgender adults. Lesbian and bisexual women were less likely to engage in all five health-related behaviors than were heterosexual women (5.4% and 6.9%, respectively, versus 10.6%). Compared with cisgender§ adults, male-to-female transgender adults were less likely to engage in any two of five health-related behaviors (12.3% versus 18.6%). Male-to-female transgender adults, however, were more likely to engage in any three of five health-related behaviors than were female-to-male transgender adults (47.2% versus 28.2%). The number of health-related behaviors did not differ between gay or bisexual men and heterosexual men. Continued efforts are needed to target LGBT populations for overall well-being, including strategies for health promotion and engagement in health-related behaviors.

Effect of Proxy Responses on Tobacco Use Surveys in Thailand, 2011.

Proxy responses are often allowed in household tobacco surveys when all household members are included in a sample. To assess the effect of proxy responses on prevalence estimates, we compared 2 surveys in 2011 that gauged tobacco use in Thailand: the Cigarette Smoking and Alcohol Drinking Survey (SADS) and the Global Adult Tobacco Survey (GATS). Both surveys had similar nonsampling errors and design, but SADS allowed proxy responses and GATS did not. When proxy responses were included in SADS, the prevalence estimate was 10% lower in GATS for men (41.69% in GATS vs 46.55% in SADS) and 18% lower in GATS for women (2.14% in GATS vs 2.61% in SADS). Eliminating proxy responses is recommended to increase accuracy of tobacco-use surveillance.

Self-Perceived Poor/Fair Health, Frequent Mental Distress, and Health Insurance Status Among Working-Aged US Adults.

We examined associations of health insurance status with self-perceived poor/fair health and frequent mental distress (FMD) among working-aged US adults from 42 states and the District of Columbia using data from the 2014 Behavioral Risk Factor Surveillance System. After multiple-variable adjustment, compared with adequately insured adults, underinsured and never insured adults were 39% and 59% more likely to report poor/fair health, respectively, and 38% more likely to report FMD. Compared with working-aged adults with employer-based insurance, adults with Medicaid/Medicare or other public insurance coverage were 28% and 13% more likely to report poor/fair health, respectively, and 15% more likely to report FMD. Increasing insurance coverage and reducing cost barriers to care may improve general and mental health.

HIV Testing Among Transgender Women and Men - 27 States and Guam, 2014-2015.

Transgender persons are at high risk for human immunodeficiency virus (HIV) infection; in a recent analysis of the results of over nine million CDC funded HIV tests, transgender women* had the highest percentage of confirmed positive results (2.7%) of any gender category (1). Transgender men,† particularly those who have sex with cisgender§ men, are also at high risk for infection (2). HIV testing is critical for detecting and treating persons who are infected and delivering preventive services to those who are uninfected. CDC recommends that persons at high risk for HIV infection be screened for HIV at least annually, although transgender persons are not specified in the current recommendations. CDC analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) to describe HIV testing among transgender women and men and two cisgender comparison groups in 27 states and Guam. After adjusting for demographic characteristics, transgender women and men had a lower prevalence of ever testing and past year testing for HIV (35.6% and 31.6% ever, and 10.0% and 10.2% past year, respectively) compared with cisgender gay and bisexual men (61.8% ever and 21.6% past year) and instead reported testing at levels comparable to cisgender heterosexual men and women (35.2% ever, and 8.6% past year). This finding suggests that transgender women and men might not be sufficiently reached by current HIV testing measures. Tailoring HIV testing activities to overcome the unique barriers faced by transgender women and men might increase rates of testing among these populations.

Prevalence of Sugar-Sweetened Beverage Intake Among Adults--23 States and the District of Columbia, 2013.

The 2015-2020 Dietary Guidelines for Americans recommend that the daily intake of calories from added sugars not exceed 10% of total calories. Sugar-sweetened beverages (SSBs) are significant sources of added sugars in the diet of U.S. adults and account for approximately one third of added sugar consumption. Among adults, frequent (i.e., at least once a day) SSB intake is associated with adverse health consequences, including obesity, type 2 diabetes, and cardiovascular disease. According to the 2009-2010 National Health and Nutrition Examination Survey (NHANES), an in-person and phone follow-up survey, 50.6% of U.S. adults consumed at least one SSB on a given day. In addition, SSB intake varies by geographical regions: the prevalence of daily SSB intake was higher among U.S. adults living in the Northeast (68.4%) and South (66.7%) than among persons living in the Midwest (58.8%). In 2013, the Behavioral Risk Factor Surveillance System (BRFSS), a telephone survey, revised the SSB two-item optional module to retain the first question on regular soda and expand the second question to include more types of SSBs than just fruit drinks. Using 2013 BRFSS data, self-reported SSB (i.e., regular soda, fruit drinks, sweet tea, and sports or energy drinks) intake among adults (aged ≥18 years) was assessed in 23 states and the District of Columbia (DC). The overall age-adjusted prevalence of SSB intake ≥1 time per day was 30.1% and ranged from 18.0% in Vermont to 47.5% in Mississippi. Overall, at least once daily SSB intake was most prevalent among adults aged 18-24 years (43.3%), men (34.1%), non-Hispanic blacks (blacks) (39.9%), unemployed adults (34.4%), and persons with less than a high school education (42.4%). States can use the data for program evaluation and monitoring trends, and information on disparities in SSB consumption could be used to create targeted intervention efforts to reduce SSB consumption.

National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS).

BACKGROUND: The Behavioral Risk Factor Surveillance System (BRFSS) is a network of health-related telephone surveys--conducted by all 50 states, the District of Columbia, and participating US territories-that receive technical assistance from CDC. Data users often aggregate BRFSS state samples for national estimates without accounting for state-level sampling, a practice that could introduce bias because the weighted distributions of the state samples do not always adhere to national demographic distributions. METHODS: This article examines six methods of reweighting, which are then compared with key health indicator estimates from the National Health Interview Survey (NHIS) based on 2013 data. RESULTS: Compared to the usual stacking approach, all of the six new methods reduce the variance of weights and design effect at the national level, and some also reduce the estimated bias. This article also provides a comparison of the methods based on the variances induced by unequal weighting as well as the bias reduction induced by raking at the national level, and recommends a preferred method. CONCLUSIONS: The new method leads to weighted distributions that more accurately reproduce national demographic characteristics. While the empirical results for key estimates were limited to a few health indicators, they also suggest reduction in potential bias and mean squared error. To the extent that survey outcomes are associated with these demographic characteristics, matching the national distributions will reduce bias in estimates of these outcomes at the national level.

Health behaviors and obesity among Hispanics with depression, United States 2006.

OBJECTIVE: To examine the differences in health behaviors, and obesity between Hispanics and non-Hispanic Whites with depression. DESIGN: Depression data were gathered from 38 states, the District of Columbia, Puerto Rico, and the US Virgin Islands using the 2006 Behavioral Risk Factor Surveillance System, a state-based random-digit-dialed telephone survey of adults aged > or =18 years (n=156,991). The Patient Health Questionnaire 8 was used to determine current depression. Lifetime diagnosis of depression was assessed by self-report of physician diagnosis. Prevalence ratios were calculated to examine the racial/ethnic differences in leisure-time physical activity, cigarette smoking, binge drinking, heavy drinking and obesity among people with current depression and lifetime diagnosis of depression. RESULTS: There were significant differences in age, education, and health care coverage between Hispanics and non-Hispanic Whites with current depression and lifetime diagnosis of depression. Hispanics with current depression and with lifetime diagnosis of depression were more likely to be obese than non-Hispanic Whites. After adjusting for demographic factors, health care coverage, and self-rated health status, Hispanics with current depression were 17% more likely not to participate in leisure-time physical activity and 42% less likely to be a current cigarette smoker compared with non-Hispanic Whites. Hispanics with lifetime diagnosis of depression were 14% more likely not to participate in leisure-time physical activity and 44% less likely to be a current cigarette smoker than non-Hispanic Whites after adjusting for confounders. CONCLUSIONS: Public health intervention programs are needed to promote healthy behaviors especially physical activity participation with in the Hispanic community, and paying particular attention to people who already are depressed.

Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019.

Introduction: Survey breakoff is an important source of total survey error. Most studies of breakoff have been of web surveys-less is known about telephone surveys. In the past decade, the breakoff rate has increased in the Behavioral Risk Factor Surveillance System, the world's largest annual telephone survey. Analysis of breakoff in Behavioral Risk Factor Surveillance System can improve the quality of Behavioral Risk Factor Surveillance System. It will also provide evidence in research of total survey error on telephone surveys. Methods: We used data recorded as breakoff in the 2018 and 2019 Behavioral Risk Factor Surveillance System. We converted questions and modules to a time variable and applied Kaplan-Meier method and a proportional hazard model to estimate the conditional and cumulative probabilities of breakoff and study the potential risk factors associated with breakoff. Results: Cumulative probability of breakoffs up to the end of the core questionnaire was 7.03% in 2018 and 9.56% in 2019. The highest conditional probability of breakoffs in the core was 2.85% for the physical activity section. Cumulative probability of breakoffs up to the end of the core was higher among those states that inserted their own questions or optional modules than among those that did not in both years. The median risk ratio of breakoff among all states was 5.70 in 2018 and 3.01 in 2019. Survey breakoff was associated with the length of the questionnaire, the extent of expected recollection, and the location of questions. Conclusions: Breakoff is not an ignorable component of total survey error and should be considered in Behavioral Risk Factor Surveillance System data analyses when variables have higher breakoff rates.

Outcomes of Population Surveillance Data Collection Pilots and the Behavioral Risk Factor Surveillance System: What Happens in Texas.

Declining response rates and rising costs have prompted the search for alternatives to traditional random-digit dialing (RDD) interviews. In 2021, three Behavioral Risk Factor Surveillance System (BRFSS) pilots were conducted in Texas: data collection using an RDD short message service (RDD SMS) text-messaging push-to-web pilot, an address-based push-to-web pilot, and an internet panel pilot. We used data from the three pilots and from the concurrent Texas BRFSS Computer Assisted Telephone Interview (CATI). We compared unweighted data from these four sources to demographic information from the American Community Survey (ACS) for Texas, comparing respondents' health information across the protocols as well as cost and response rates. Non-Hispanic White adults and college graduates disproportionately responded in all survey protocols. Comparing costs across protocols was difficult due to the differences in methods and overhead, but some cost comparisons could be made. The cost per complete for BRFSS/CATI ranged from $75 to $100, compared with costs per complete for address-based sampling ($31 to $39), RDD SMS ($12 to $20), and internet panel (approximately $25). There were notable differences among survey protocols and the ACS in age, race/ethnicity, education, and marital status. We found minimal differences in respondents' answers to heart disease-related questions; however, responses to flu vaccination questions differed by protocol. Comparable responses were encouraging. Properly weighted web-based data collection may help use data collected by new protocols as a supplement to future BRFSS efforts.

Prevalence of HIV risk behaviors between binge drinkers and non-binge drinkers aged 18- to 64-years in US, 2008.

Using data from the 2008 Behavioral Risk Factor Surveillance System on 281,303 adults aged 18-64 years in the United States, we examined the relationship between HIV risk behaviors and binge drinking of alcoholic beverages and the frequency of binge drinking among a subgroup of 41,073 respondents who were acknowledged binge drinkers (bingers), based on reported drinking behavior in the year preceding survey. Our findings show that the weighted prevalence of HIV risk behaviors (including injection drug use, exchange of sex for money/drugs, and anal sex without a condom) among binge drinkers [corrected] [7.0%, 95% confidence interval (95% CI): 6.4-7.6%] is twice that among nonbingers (2.9%, 95% CI: 2.7-3.0%). The highest prevalence of HIV risk behaviors is among the bingers aged 18-20 years (14%, 95% CI: 11.2-18.2%). After adjusting for covariates, bingers are 1.77 (95% CI: 1.58-2.00) times more likely than nonbingers to report HIV risk behaviors. Risk increases in bingers with the number of episodes. Compared with bingers reporting 1-2 binge episodes in the month proceeding survey, the adjusted odds of reporting HIV risk behaviors among bingers are 1.27 (1.08-1.49), 1.68 (1.35-2.10), 1.67 (1.08-2.57), and 1.70 (1.34-2.16), respectively for bingers with 3-4, 5-6, 7-8, and ≥9 episodes in the same period. Our results suggest that HIV risk behaviors are strongly linked with binge drinking and its frequency. Effective measures to prevent binge drinking are essential to HIV prevention, especially among youth aged 18-20 years.

Binge drinking intensity and health-related quality of life among US adult binge drinkers.

INTRODUCTION: Binge drinking (men, ≥ 5 drinks, women, ≥ 4 on an occasion) accounts for more than half of the 79,000 annual deaths due to excessive alcohol use in the United States. The frequency of binge drinking is associated with poor health-related quality of life (HRQOL), but the association between binge drinking intensity and HRQOL is unknown. Our objective was to examine this association. METHODS: We used 2008-2010 Behavioral Risk Factor Surveillance System data and multivariate linear regression models to examine the association between binge drinking intensity (largest number of drinks consumed on any occasion) among US adult binge drinkers and 2 HRQOL indicators: number of physically and mentally unhealthy days. RESULTS: Among binge drinkers, the highest-intensity binge drinkers (women consuming ≥ 7 drinks and men consuming ≥ 8 drinks on any occasion) were more likely to report poor HRQOL than binge drinkers who reported lower levels of intensity (women who consumed 4 drinks and men who consumed 5 drinks on any occasion). On average, female binge drinkers reported more physically and mentally unhealthy days (2.8 d and 5.1 d, respectively) than male binge drinkers (2.5 d and 3.6 d, respectively). After adjustment for confounding factors, women who consumed ≥ 7 drinks on any occasion reported more mentally unhealthy days (6.3 d) than women who consumed 4 drinks (4.6 d). Compared with male binge drinkers across the age groups, female binge drinkers had a significantly higher mean number of mentally unhealthy days. CONCLUSION: Our findings underscore the importance of implementing effective population-level strategies to prevent binge drinking and improve HRQOL.