Yu-Hsiu Lin1, Alexander C McLain2, Janice C Probst3, Kevin J Bennett4, Zaina P Qureshi2, Jan M Eberth5. 1. National Institute of Environmental Health Research Center, National Health Research Institutes, Taiwan. 2. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia. 3. Department of Health Services Policy & Management, University of South Carolina, Columbia; South Carolina Rural Health Research Center, University of South Carolina, Columbia. 4. South Carolina Rural Health Research Center, University of South Carolina, Columbia; Department of Family and Preventive Medicine, School of Medicine, University of South Carolina, Columbia. 5. Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia; South Carolina Rural Health Research Center, University of South Carolina, Columbia; Cancer Prevention and Control Program, University of South Carolina, Columbia. Electronic address: jmeberth@mailbox.sc.edu.
Abstract
PURPOSE: The purpose of this study was to develop county-level estimates of poor health-related quality of life (HRQOL) among aged 65 years and older U.S. adults and to identify spatial clusters of poor HRQOL using a multilevel, poststratification approach. METHODS: Multilevel, random-intercept models were fit to HRQOL data (two domains: physical health and mental health) from the 2011-2012 Behavioral Risk Factor Surveillance System. Using a poststratification, small area estimation approach, we generated county-level probabilities of having poor HRQOL for each domain in U.S. adults aged 65 and older, and validated our model-based estimates against state and county direct estimates. RESULTS: County-level estimates of poor HRQOL in the United States ranged from 18.07% to 44.81% for physical health and 14.77% to 37.86% for mental health. Correlations between model-based and direct estimates were higher for physical than mental HRQOL. Counties located in the Arkansas, Kentucky, and Mississippi exhibited the worst physical HRQOL scores, but this pattern did not hold for mental HRQOL, which had the highest probability of mentally unhealthy days in Illinois, Indiana, and Vermont. CONCLUSIONS: Substantial geographic variation in physical and mental HRQOL scores exists among older U.S. adults. State and local policy makers should consider these local conditions in targeting interventions and policies to counties with high levels of poor HRQOL scores.
PURPOSE: The purpose of this study was to develop county-level estimates of poor health-related quality of life (HRQOL) among aged 65 years and older U.S. adults and to identify spatial clusters of poor HRQOL using a multilevel, poststratification approach. METHODS: Multilevel, random-intercept models were fit to HRQOL data (two domains: physical health and mental health) from the 2011-2012 Behavioral Risk Factor Surveillance System. Using a poststratification, small area estimation approach, we generated county-level probabilities of having poor HRQOL for each domain in U.S. adults aged 65 and older, and validated our model-based estimates against state and county direct estimates. RESULTS: County-level estimates of poor HRQOL in the United States ranged from 18.07% to 44.81% for physical health and 14.77% to 37.86% for mental health. Correlations between model-based and direct estimates were higher for physical than mental HRQOL. Counties located in the Arkansas, Kentucky, and Mississippi exhibited the worst physical HRQOL scores, but this pattern did not hold for mental HRQOL, which had the highest probability of mentally unhealthy days in Illinois, Indiana, and Vermont. CONCLUSIONS: Substantial geographic variation in physical and mental HRQOL scores exists among older U.S. adults. State and local policy makers should consider these local conditions in targeting interventions and policies to counties with high levels of poor HRQOL scores.
Authors: Anja Zgodic; Jan M Eberth; Charity B Breneman; Marilyn E Wende; Andrew T Kaczynski; Angela D Liese; Alexander C McLain Journal: Am J Epidemiol Date: 2021-12-01 Impact factor: 5.363