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Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

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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
    Department of Bioengineering, 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)

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.

Suggested Citation

  • Brian Witrick & Corey A. Kalbaugh & Lu Shi & Rachel Mayo & Brian Hendricks, 2021. "Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2021:i:1:p:285-:d:712574
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    References listed on IDEAS

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    1. Jonathan E. Helm & Adel Alaeddini & Jon M. Stauffer & Kurt M. Bretthauer & Ted A. Skolarus, 2016. "Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization," Production and Operations Management, Production and Operations Management Society, vol. 25(2), pages 233-257, February.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    4. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
    5. Naeimehossadat Asmarian & Seyyed Mohammad Taghi Ayatollahi & Zahra Sharafi & Najaf Zare, 2019. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran," IJERPH, MDPI, vol. 16(22), pages 1-13, November.
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