Document Type
Article
Publication Date
2021
Advisor
Amber Todd
Abstract
Objective: The objective of this study is to analyze data to determine the factors that influence preventable hospital stays in all Maryland counties. Explicitly, I am examining how preventable hospital stays changes over the past eight years, comparing preventable hospital stays in Maryland and Virginia, correlating preventable hospital stays with Flu vaccinations, and lastly, examining how the rate of uninsured and income inequality can predict the variance in preventable hospital stays by county.
Methods: The data I will be using is sourced from the 2020 County Health Rankings and Roadmaps report. The most recent data available in this published 2020 report is from the year 2017. I will be utilizing paired t-tests, unpaired t-tests, Pearson/Spearman correlations, and stepwise linear regressions to analyze the data.
Results: A paired t-test analysis was used to compare the rate of preventable hospital stays (which the data source defines as the number of hospital stays for ambulatory-care sensitive conditions per 100,000 Medicare enrollees 11) in Maryland counties in 2012 versus 2020. Wefound that the rate of preventable hospital stays significantly decreased from 7284.36 per 100,000 Medicare enrollees in 2012, to 4419.04 per 100,000 Medicare enrollees in 2020 (t = 11.663, p < .001). An unpaired t-test analysis was used to compare the rates of preventable hospital stays between Maryland (4419.04) and Virginia (4808.02) counties in 2020 (t = -1.503, p = .135). We found that a statistically significant difference does not exist between the states’ preventable hospital stays rates. A Spearman correlation analysis indicated a small but significant negative correlation (r = -0.615, p = .001) between the percentage of flu vaccinations and the rate of preventable hospital stays. The outcome of the linear regression analyses of rate of uninsured and income inequality was determined to be insignificant (F2,21 = 0.784, p < 0.469), with a weak accounting for 7% of the variance in preventable hospital stays. Neither the rate of uninsured nor income inequality contributed significantly to the strength of the model. Therefore, we determined that these two variables are not good indicators of preventable hospital stays.
Repository Citation
Lubov, J. (2021). Exploring Preventable Hospital Stays in Maryland. Wright State University. Dayton, Ohio.
