Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Brian Witrick, Corey A. Kalbaugh, Lu Shi, Rachel Mayo and Brian Hendricks
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Brian Witrick: Department of Public Health Sciences, Clemson University, Clemson, SC 29631, USA
Corey A. Kalbaugh: Department of Public Health Sciences, Clemson University, Clemson, SC 29631, USA
Lu Shi: Department of Public Health Sciences, Clemson University, Clemson, SC 29631, USA
Rachel Mayo: Department of Public Health Sciences, Clemson University, Clemson, SC 29631, USA
Brian Hendricks: Department of Epidemiology and Biostatistics, West Virginia University School of Public Health, Morgantown, WV 26505, USA

IJERPH, 2021, vol. 19, issue 1, 1-11

Abstract: Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Keywords: peripheral artery disease; disparities; spatial analysis; readmission (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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