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.

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.

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.

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.

Estimating bias from internet non-use for a hybrid web vaccination survey - 2013-2017 Behavioral Risk Factor Surveillance System.

OBJECTIVE: To maintain acceptable response rates, the cost has grown for vaccination surveys that use traditional data collection modes, such as face-to-face and telephone interviews. Conducting a web or internet survey could be a low-cost alternative. However, because the internet is not used by everyone, we need to study how prevalence estimates in web surveys for vaccination surveillance could be affected by internet non-use. METHOD: We analyzed data from the 2013-2017 Behavioral Risk Factor Surveillance System to assess undercoverage biases from internet non-use by partitioning into proportion of internet non-users and difference in prevalence of influenza and pneumococcal vaccinations between internet and internet non-users, respectively. RESULTS: The proportion of internet non-users decreased monotonically from 43.3% in 2013 to 35.4% in 2017; however, the undercoverage bias from internet use for pneumococcal vaccination increased from 0.8 to 1.5 percentage points at the same time. Overall, the undercoverage bias was -1.1 and 1.5 percentage points for influenza vaccination and pneumococcal vaccination in 2017, respectively. For both vaccinations, we found large absolute and relative biases among certain demographic subgroups. CONCLUSIONS: Although the proportion of internet non-users decreased in recent years, undercoverage bias of hybrid internet survey for influenza and pneumococcal vaccinations did not decrease. Despite a small overall undercoverage bias, the bias in subpopulation groups was not negligible.

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.

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.

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.

Comparisons of Estimates From the Behavioral Risk Factor Surveillance System and Other National Health Surveys, 2011-2016.

INTRODUCTION: The Behavioral Risk Factor Surveillance System (BRFSS) is composed of telephone surveys that collect state data from non-institutionalized U.S. adults regarding health-related risk behaviors and chronic health conditions. A new design was implemented in 2011 to include participants on cellular telephones. It is important to validate estimates since 2011. METHODS: A total of 10 key and widely used variables between BRFSS and the National Health and Nutrition Examination Survey (NHANES) or National Health Interview Survey (NHIS) in 2011-2016 were compared. Data analysis was conducted in 2018. RESULTS: Between BRFSS and NHANES, similar linear time trends of prevalences or means were found for 8 of 9 studied variables. There were no significant differences in the prevalences of the following variables: self-reported fair/poor health, ever told have diabetes, and ever told to have hypertension. In trend comparison of BRFSS versus NHIS, interactions of prevalence between survey and time period were not found for 5 variables: current smoking, self-reported fair/poor health, ever told have diabetes, and self-reported height and weight. Although there were significant differences in many estimates between BRFSS and either NHANES or NHIS, the absolute differences across years were rather small. CONCLUSIONS: Comparing BRFSS time trends with those of 2 national benchmark surveys in 10 key and widely used variables suggests that the trends of prevalences (or means) from BRFSS, NHANES, and NHIS are mostly similar. For many variables, despite statistically significant differences in the prevalences (or means) between surveys, absolute differences in most cases were small and not meaningful from a public health surveillance perspective.

Prevalence of Disability and Disability Types by Urban-Rural County Classification-U.S., 2016.

INTRODUCTION: In the U.S., disability affects approximately 61.4 million (25.7%) adults, with mobility disability being the most prevalent type, affecting about 1 in 7 U.S. adults. However, little is known about the prevalence of disability and functional disability types by urbanization level. METHODS: Data from the 2016 Behavioral Risk Factor Surveillance System were analyzed. The prevalences of disability, overall and by functional disability type, were estimated among U.S. adults across 6 levels of urban-rural county categories based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Adjusted prevalence ratios with 95% CIs were estimated by conducting log-linear regression analyses with robust variance estimator while adjusting for study covariates. Data analyses were conducted in 2018. RESULTS: The prevalences of having any disability, functional disability type, or multiple disabilities were lowest in large metropolitan centers and fringe metropolitan counties and highest in noncore (rural) counties. After controlling for age, sex, race/ethnicity, education, and federal poverty level, adults living in noncore counties were 9% more likely to report having any disability, 24% more likely to report having 3 or more disabilities, and 7% (cognition) to 35% (hearing) more likely to report specific disability types than the adults living in large metropolitan centers. CONCLUSIONS: Results of this study suggest that significant disparities in the prevalence of disability exist by level of urbanization, with rural U.S. residents having the highest prevalence of disability. Public health interventions to reduce health disparities could include people with disabilities, particularly in rural counties.

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.

Differences in Efficiencies Between ABS and RDD Samples by Mode of Data Collection.

Research on mode of administration of surveys increasingly appears in the literature. Little research includes comparisons by sample frame as well as by mode. This research examines differences in efficiency using two types of sample frames (address-based samples [ABS] or random digit dialing [RDD] samples) and multiple modes (web-based surveys, mailed questionnaire, and telephone interview) among adult respondents. Matching telephone numbers to addresses was conducted on both samples. A test of the effectiveness of making modifications to drop point locations in the ABS was also undertaken. A higher proportion of addresses were matched to telephone numbers in an ABS but with less accuracy than matching telephone numbers to addresses in an RDD sample. Costs per competed interview were lower using the RDD than when using the ABS. Efforts to specify apartment numbers in drop point locations in the ABS were not found to be cost effective. Overall, for both demographic and substantive question outcomes, survey frame has less of an impact than survey mode on measures of response rate and cost.

Sample and Respondent Provided County Comparisons Among Cellular Respondents Using Rate Center Assignments.

The percentage of cell phones in telephone survey samples continues to grow in proportion to the percentage of potential respondents who rely on cell phones for personal communication. One problem with cell phone samples is that persons who move or who purchase cell phones in locations not close to their residence, may not be eligible for surveys with geographic parameters. This affects researchers' ability to sample and analyze from specific geographic jurisdictions. Because cell phone numbers do not accurately indicate respondent locations, rate centers have been used to ascertain respondent locations in recent years. The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based telephone survey administered to over 400,000 respondents annually. Approximately half of the sample is drawn from cell phone numbers. This research examines the county-level accuracy of the 2016 BRFSS sample. Results indicate that cell phone samples are accurate at the state and county level 58% of the time and at just the state level 93% of the time. However, accuracy rates vary by state, region, metropolitan status as well as by demographic characteristics and survey items. Specific examples of when county-level accuracy vary are provided.

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.

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.

Hearing Disability Prevalence and Risk Factors in Two Recent National Surveys.

INTRODUCTION: Hearing loss is a worldwide societal and public health concern. Globally, disabling hearing loss affects 538 million adults (men, 12.2%; women, 9.8%). This study examined the prevalence and risk factors associated with deafness or serious difficulty hearing in two nationally representative surveys. METHODS: Data were analyzed in 2017 from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) and the 2014 National Health Interview Survey. The BRFSS collected data through telephone interviews. The 2014 National Health Interview Survey collected face-to-face household interview data that included a hearing health supplement in the Sample Adult Core. Both surveys asked adults aged ≥18 years the disability question on deafness or serious difficulty hearing as defined by the American Community Survey. Weighted prevalence, prevalence ratios, and 95% CIs were calculated. Logistic regression was used to adjust for sociodemographic and geographic characteristics. RESULTS: Prevalence of deafness or serious difficulty hearing was 5.8% (BRFSS) and 6.0% (National Health Interview Survey); males had a 60% higher prevalence than females. The prevalence was significantly associated with increasing age, lower educational level and income, and was higher among non-Hispanic whites than among non-Hispanic blacks and Hispanics. Deafness or serious difficulty hearing was strongly associated with increasing degree of self-reported trouble hearing in the National Health Interview Survey. The BRFSS state-specific prevalence varied from 3.8% to 13.3%, with higher prevalence in the most public health-challenged states according to America's Health Rankings. CONCLUSIONS: The prevalence of deafness or serious difficulty hearing was approximately 6% in the National Health Interview Survey and BRFSS, but increased considerably for older, less advantaged individuals and in more public health-challenged states.