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南卡罗来纳州外周动脉疾病再入院的地域差异。

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina.

机构信息

Department of Public Health Sciences, Clemson University, Clemson, SC 29631, USA.

Department of Bioengineering, Clemson University, Clemson, SC 29631, USA.

出版信息

Int J Environ Res Public Health. 2021 Dec 28;19(1):285. doi: 10.3390/ijerph19010285.

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.

摘要

再入院是外周动脉疾病(PAD)患者的主要医疗负担。本研究旨在:1)估计 PAD 患者的邮政编码区(ZCTA)再入院率,并描述协变量对再入院的影响;2)基于再入院估计率识别 PAD 的热点地区。从南卡罗来纳州收入和财政事务办公室所有支付者数据库(2010-2018 年)中确定了 PAD 患者的 30 天再入院情况。采用贝叶斯空间层次模型来识别高风险区域,同时控制混杂因素。我们绘制了估计的再入院率图,并使用局部 Getis Ord(G*)统计数据确定了热点地区。在 232731 名因 PAD 诊断而住院或门诊手术的患者中,有 30366 名(13.1%)在 30 天内计划外再次入院。拟合的再入院率范围从每 1000 名患者 35.3 例到每 1000 名患者 370.7 例,再入院的风险与 65 岁及以上患者的比例(0.992,95%CI:0.985-0.999)、拥有医疗保险(1.013,1.005-1.020)和患有高血压(1.014,1.005-1.023)显著相关。地理分析发现全州范围内再入院率存在显著差异,并确定了重点干预以减少再入院的优先领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a1a0/8751080/320883ac22aa/ijerph-19-00285-g001.jpg

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