Defense Date


Document Type


Degree Name

Doctor of Philosophy


Healthcare Policy & Research

First Advisor

Bassam A. Dahman, Ph.D.

Second Advisor

Peter J. Cunningham, Ph.D.

Third Advisor

Juan Lu, PhD, MPH, MD

Fourth Advisor

Askar S. Chukmaitov, M.D., Ph.D., M.P.A.


Background: The 30-day readmission rate is considered a quality of care measure for providers and has become important because providers might face reduced reimbursement from any increase in unplanned readmissions

Objective: The aim of the first chapter is to investigate the waiting-length of stay (WLOS) and post-length of stay (PLOS) on the 30-day readmission. In the second chapter, we examined the hospital procedural volume and hospital quality on the 30-day readmission. Our objective in the third chapter is to examine the zip code-level SES factors on the 30-day readmission rates.

Participants: patients undergoing isolated coronary artery bypass grafting (CABG) in Virginia

Methods: A retrospective study design has been conducted using a multi-level logistic model of increasing complexity for all three chapters. The sample used was from the Virginia Cardiac Surgery Quality Initiative (VCSQI) of the periods 2008-2014, the dataset included patient characteristics. Afterward, we merged the sample with both the Virginia Health Information (VHI) to obtain hospital characteristics (ownership, teaching status, and location), and Agency for Healthcare Research and Quality (AHRF) to obtain county-socio-economic status (SES) characteristics (education, employment, and median household income), the previous SES was used for chapter’s one and two. In chapter three, instead of AHRF, we merged the sample with the American Community Survey (ACS) to obtain zip code-SES characteristics (employment, median household income, education, median house price). The main outcome was the 30-day readmission rate. The analytical sample of chapter one n = 22,097, in chapter two the sample n = 25,531, while in chapter three the sample n= 25,829. We conducted a sensitivity analysis in all three chapters. In chapter one we analyzed the data at the patient level, in chapter two we analyzed the data at the hospital level, while in chapter three we conducted the analysis at the area zip code level.

Results: In chapter one, we found that readmitted patients after a prolonged PLOS had increased odds of readmission, by 68.7%, compared to readmitted patients with a shorter PLOS in the fully adjusted model; while, WLOS was not significant at the P < 0.05. In chapter two, the fully adjusted model displayed significant results with a reduced odds in readmissions by 22.8% in the middle-volume hospitals compared to the low-volume hospitals, while the middle-quality hospitals had increased odds of readmission by 23.5% compared to the low-quality hospitals. In chapter three, statistically, we did not find that area zip code-SES had an effect on the 30-day readmission rate. While, geographically, we found that addresses of individuals were clustered in certain areas of Virginia.

Conclusion: In chapter one, patients undergoing CABG and experience a prolonged PLOS of > 6 days are at risk to be readmitted within 30-days of the procedure. In chapter two, the higher volume hospitals (middle-volume) compared to low-volume hospitals showed a significant reduction in odds in the 30-day readmissions, especially after adjusting the model with hospital quality. In chapter three, even though, there was no association of area-SES with 30-day readmission, in the maps, we found a cluster of patient addresses in the southern parts of Virginia with an increased readmission, which is considered underprivileged area; and the fact might be due to the proximity of these areas to cardiovascular hospitals.

Policy Implication: In chapter one, the study provided a model for clinicians to stratify patients at risk of readmission, especially patients with risks of staying longer in the hospital after CABG. In chapter two, policymakers and the CMS should find new ways to help hospitals with low-volumes to reduce their isolated-CABG readmission rates and be able to compete with high-volume hospitals. In chapter three, no significant correlation between area-SES and readmission for patients who underwent CABG was found; these backs prior notion that SES should not be adjusted for the reimbursement penalties of the Hospital Readmission Reductions Program (HRRP) on hospitals


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Available for download on Monday, June 21, 2219