RESUMEN
Background: The goal of this study was to re-estimate rates of bilateral hearing loss Nationally, and create new estimates of hearing loss prevalence at the U.S. State and County levels. Methods: We developed small area estimation models of mild, and moderate or worse bilateral hearing loss in the U.S. using data from the National Health and Nutrition Examination Survey (2001-2012, 2015-2018), the American Community Survey (2019), Census County Business Patterns (2019); Social Security Administration Data (2019); Medicare Fee-for-Service and Advantage claims data (2019); the Area Health Resources File (2019), and other sources. We defined hearing loss as mild (>25 dB through 40 dB), moderate or worse (>40 dB), or any (>25 dB) in the better hearing ear based on a 4-frequency pure-tone-average threshold, and created estimates by age group (0-4, 5-17, 18-34, 35-64, 65-74, 75+), gender, race and ethnicity, state, and county. Findings: We estimated that 37.9 million (95% Uncertainty Interval [U.I.] 36.6-39.1) Americans experienced any bilateral hearing loss; 24.9 million (95% U.I. 23.6-26.0) with mild and 13.0 million (95% U.I. 12.1-13.9) with moderate or worse. The prevalence rate of any hearing loss was 11.6% (95% U.I. 11.2%-12.0%). Hearing loss increased with age. Men were more likely to have hearing loss than women after age 35, and non-Hispanic Whites had higher rates of hearing loss than other races and ethnicities. Higher hearing loss prevalence was associated with smaller population size. West Virginia, Alaska, Wyoming, Oklahoma, and Arkansas had the highest standardised rate of bilateral hearing loss, and Washington D.C., New Jersey, New York, Maryland, and Connecticut had the lowest. Interpretation: Bilateral Hearing loss varies by State and County, with variation associated with population age, race and ethnicity, and population size. Geographic estimates can be used to raise local awareness of hearing loss as a problem, to prioritize areas for hearing loss prevention, identification, and treatment, and to guide future research on the hearing loss risk factors that contribute to these differences. Funding: CDC's National Center for Chronic Disease Prevention and Health Promotion, Division of Population Health.
RESUMEN
Purpose: People with vision problems (VPs) have different needs based on their age, economic resources, housing type, neighborhood, and other disabilities. We used calibration methods to create synthetic data to estimate census tract-level community need profiles (CNPs) for the city of Richmond, Virginia. Design: Cross-sectional secondary data analysis. Subjects: Anonymized respondents to the 2015 to 2019 American Community Survey (ACS). Methods: We used calibration methods to transform the ACS 5-year tabular (2015-2019) and Public Use Microdata estimates into a synthetic data set of person-level records in each census tract, and subset the data to persons who answered yes to the question "Are you blind or do you have serious difficulty seeing even when wearing glasses?" To identify individual need profiles (INPs), we applied divisive clustering to 17 variables measuring individual demographics, nonvision disability status, socioeconomic status (SES), housing, and access and independence. We labeled tracts with CNP names based on their predominant INPs and performed sensitivity analyses. We mapped the CNPs and overlayed information on the number of people with VP, the National Walkability Index, and an uncertainty measure based on our sensitivity analysis. Main Outcome Measures: Individual need profiles and CNPs. Results: Compared with people without VP, people with VP exhibited higher rates of disabilities, having low incomes, living alone, and lacking access to the internet or private home vehicles. Among people with VP, we identified 7 INP clusters which we mapped into 6 CNPs: (1) seniors (≥ age 65); (2) low SES younger; (3) low SES older; (4) mixed SES; (5) higher SES; and (6) adults and children in group quarters. Three CNPs had lower-than-average walkability. Community need profile assignments were somewhat sensitive to calibration variables, with 18 tracts changing assignments in 1 sensitivity analysis, and 4 tracts changing assignments in ≥ 2 sensitivity analyses. Conclusions: This pilot project illustrates the feasibility of using ACS data to better understand the support and service needs of people with VP at the census tract level. However, a subset of categorical CNP assignments were sensitive to variable selection leading to uncertainty in CNP assignment in certain tracts. Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
