Carla Shoff1, Kadin Caines2, Jesse M Pines3. 1. Centers for Medicare & Medicaid Services, Baltimore, MD, United States. Electronic address: carla.shoff@cms.hhs.gov. 2. Kaiser Permanente, Rockville, MD, United States. Electronic address: Kadin.J.Caines@kp.org. 3. US Acute Care Solution (USACS), Canton, OH. Electronic address: pinesj@usacs.com.
Abstract
INTRODUCTION: We study community-level factors associated with emergency department (ED) admission rates and assessed how they vary across geography. METHODS: We conducted an ecological study using 2012 data from 100% of U.S. Medicare Fee-for-Service beneficiaries to calculate county-level ED admission rates, adjusted by Hierarchical Condition Categories to control for patient health. We tested community-level measures related to healthcare market concentration, healthcare delivery, and socioeconomic factors potentially associated with admission rates and assessed whether these factors predicted ED admission rates across counties using ordinary least squares (OLS) regression and whether they varied across geography using geographically weighted regression (GWR). RESULTS: In 3031 U.S. counties, the ED admission rate varied from 3.9% to 82.2%. The lowest ED admission rates were concentrated in counties in Kansas, Oregon, and Vermont and the highest ED admission rates were in counties throughout Washington, Wyoming, Texas, and Colorado. The OLS model found several community-level factors that negatively impacted admission rates, specifically hospital market concentration, the rate of hospital beds with urgent care, and the rate of hospital beds. The factors that had a positive impact on the admission rate include the rate of MDs and factors for disadvantage, affluence, and foreign born/Hispanic. However, GWR showed the relationship between the ED admission rate and predictors varied across U.S. counties CONCLUSIONS: The association between healthcare market concentration, healthcare delivery, and socioeconomic factors with ED admissions differed across communities in Medicare beneficiaries. This suggests that policy and interventions to reduce ED admissions need to be tailored to specific community contexts.
INTRODUCTION: We study community-level factors associated with emergency department (ED) admission rates and assessed how they vary across geography. METHODS: We conducted an ecological study using 2012 data from 100% of U.S. Medicare Fee-for-Service beneficiaries to calculate county-level ED admission rates, adjusted by Hierarchical Condition Categories to control for patient health. We tested community-level measures related to healthcare market concentration, healthcare delivery, and socioeconomic factors potentially associated with admission rates and assessed whether these factors predicted ED admission rates across counties using ordinary least squares (OLS) regression and whether they varied across geography using geographically weighted regression (GWR). RESULTS: In 3031 U.S. counties, the ED admission rate varied from 3.9% to 82.2%. The lowest ED admission rates were concentrated in counties in Kansas, Oregon, and Vermont and the highest ED admission rates were in counties throughout Washington, Wyoming, Texas, and Colorado. The OLS model found several community-level factors that negatively impacted admission rates, specifically hospital market concentration, the rate of hospital beds with urgent care, and the rate of hospital beds. The factors that had a positive impact on the admission rate include the rate of MDs and factors for disadvantage, affluence, and foreign born/Hispanic. However, GWR showed the relationship between the ED admission rate and predictors varied across U.S. counties CONCLUSIONS: The association between healthcare market concentration, healthcare delivery, and socioeconomic factors with ED admissions differed across communities in Medicare beneficiaries. This suggests that policy and interventions to reduce ED admissions need to be tailored to specific community contexts.
Authors: Alexander S Qian; Edmund M Qiao; Vinit Nalawade; Rohith S Voora; Nikhil V Kotha; Christian Dameff; Christopher J Coyne; James D Murphy Journal: Cancer Med Date: 2021-11-24 Impact factor: 4.452