Patient-reported outcomes and costs associated with vascular closure and same-day discharge following atrial fibrillation ablation.

BACKGROUND: We aimed to measure patient-reported outcomes (PROs) and costs associated with same-day discharge (SDD) for atrial fibrillation (AF) ablation and vascular closure device implantation in clinical practice. METHODS: PROs were prospectively measured in 50 AF ablation patients, comparing complete vascular device closure (n = 25) versus manual compression hemostasis (n = 25). Health-system costs for SDD patients receiving vascular device closure were compared to matched controls with one-night stays who did not receive any closure device. RESULTS: Prospectively enrolled patients receiving vascular device closure for AF ablation had a mean age of 65 years, 17% were female, with a mean CHA2 DS2 -VASc score of 3. The mean number of venous sheaths was higher among patients receiving vascular device closure (3.8 vs. 3.1, p < 0.001), and there was one case of rebleeding in a patient receiving a vascular closure device (no other complications). Same-day discharge rates (76% vs. 8.3%, p < 0.001), patient satisfaction with bedrest time (8.5 vs. 6, p = 0.004) and with pain (8 vs. 5.1, p = 0.009) were significantly better among patients receiving vascular closure. In matched analyses of health-system costs, patients with vascular closure had mean age 66, 32% were female, and the mean CHA2 DS2 -VASc score was 2 (p = NS vs. controls). SDD with vascular closure was associated with the significantly lower facility, pharmacy, and disposable costs, but higher implant costs. Overall costs for ablation were not significantly different (mean difference 1.10%, 95% confidence interval [CI] -3.03 to 5.42). CONCLUSIONS: Vascular closure for AF ablation improves patient experience in routine care. The use of vascular closure and SDD after AF ablation reduces several components of healthcare system costs, without an overall increase.

Impact of Coal Mining on Self-Rated Health among Appalachian Residents.

OBJECTIVE: To determine the impact of coal mining, measured as the number of coal mining-related facilities nearby one's residence or employment in an occupation directly related to coal mining, on self-rated health in Appalachia. METHODS: Unadjusted and adjusted ordinal logistic regression models calculated odds ratio estimates and associated 95% confidence intervals for the probability of having an excellent self-rated health response versus another response. Covariates considered in the analyses included number of coal mining-related facilities nearby one's residence and employment in an occupation directly related to coal mining, as well as potential confounders age, sex, BMI, smoking status, income, and education. RESULTS: The number of coal mining facilities near the respondent's residence was not a statistically significant predictor of self-rated health. Employment in a coal-related occupation was a statistically significant predictor of self-rated health univariably; however, after adjusting for potential confounders, it was no longer a significant predictor. CONCLUSIONS: Self-rated health does not seem to be associated with residential proximity to coal mining facilities or employment in the coal industry. Future research should consider additional measures for the impact of coal mining.

Comparison of Mortality Disparities in Central Appalachian Coal- and Non-Coal-Mining Counties.

OBJECTIVE: Determine whether select cause of death mortality disparities in four Appalachian regions is associated with coal mining or other factors. METHODS: We calculated direct age-adjusted mortality rates and associated 95% confidence intervals by sex and study group for each cause of death over 5-year time periods from 1960 to 2009 and compared mean demographic and socioeconomic values between study groups via two-sample t tests. RESULTS: Compared with non-coal-mining areas, we found higher rates of poverty in West Virginia and Virginia (VA) coal counties. All-cause mortality rates for males and females were higher in coal counties across all time periods. Virginia coal counties had statistically significant excesses for many causes of death. CONCLUSIONS: We found elevated mortality and poverty rates in coal-mining compared with non-coal-mining areas of West Virginia and VA. Future research should examine these findings in more detail at the individual level.

General mortality patterns in appalachian coal-mining and non-coal-mining counties.

OBJECTIVE: To determine whether mortality disparities in Appalachia are due to coal mining or other factors. METHODS: Unadjusted and covariate adjusted rate ratio models calculated total, all external, and all cancer mortality rates from 1960 to 2009 for cumulative total, surface, and underground coal production in coal-mining counties compared with non-coal-mining counties. RESULTS: No coal-related statistically significant elevations in total or all external mortality were found. Control for covariates attenuated rate ratios for all levels of coal mining. All forms of coal were statistically significant in the adjusted rate ratio models for all cancer mortality, with 4% to 6% excesses in the highest quartiles of production. CONCLUSIONS: Total and all external mortalities do not seem to be related to coal production in Appalachia, but all cancer mortality should be further examined. Additional causes of death should also be considered.