https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:27022 Wed 11 Apr 2018 16:56:42 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:15194 Wed 11 Apr 2018 15:37:49 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:26622 Wed 11 Apr 2018 15:14:56 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:37864 Mon 24 May 2021 12:44:01 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:52507 Mon 16 Oct 2023 10:23:32 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:46241 N = 1432 women diagnosed with AF from 2000 to 2015 of the old cohort (born 1921–26) of the Australian Longitudinal Study on Women’s Health (ALSWH) who remained alive for at least 12 months post first recorded AF diagnosis. Self-reported data on demographics, health behaviours, health conditions, and SF-36 were obtained from the ALSWH surveys, corresponding to within three years of the date of the first record of AF diagnosis. Linked Pharmaceutical Benefits Scheme (PBS) data determined the use of oral anticoagulants and comorbid conditions, included in CHA₂DS₂-VA (Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes, Stroke or TIA, Vascular disease and Age 65–74 years) score calculation, were assessed using state-based hospital admissions data. Utility scores were calculated for every woman from their SF-36 responses using the SF-6D algorithm with Australian population norms. Mean utility scores were then calculated for women with various demographic, health behaviours, and clinical characteristics. Ordinary Least Square (OLS) regression modelling was performed to determine factors associated with these utility scores. Two different scenarios were used for the analysis: (1) complete-case, for women with complete data on all the SF-36 items required to estimate SF-6D (N = 584 women), and (2) Multiple Imputation (MI) for missing data, applied to missing values on SF-36 items (N = 1432 women). MI scenario was included to gauge the potential bias when using complete data only. Results: The mean health utility was estimated to be 0.638 ± 0.119 for the complete dataset and 0.642 ± 0.120 for the dataset where missing values were handled using MI. Using the MI technique, living in regional and remote areas (β=0.016±0.007) and the use of oral anticoagulants (β=0.021±0.007) were positively associated with health utility compared to living in major cities and no use of anticoagulants, respectively. Difficulty to manage on available income (β=−0.027±0.009), no/low physical activity (β=−0.069±0.011), disability (β=−0.097±0.008), history of stroke (β=−0.025±0.013) and history of arthritis (β=−0.024±0.007) were negatively associated with health utility. Conclusion: This study presents health utility estimates for older women with AF. These estimates can be used in future clinical and economic research. The study also highlights better health utilities for women living in regional and remote areas, which requires further exploration.]]> Mon 14 Nov 2022 13:17:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51916 Fri 22 Sep 2023 10:40:39 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:55281 Fri 10 May 2024 16:13:47 AEST ]]>