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Catchment Area Analysis Using Bayesian Regression Modeling

机译:贝叶斯回归模型的流域面积分析

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摘要

A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research, we constructed both diagnosis and diagnosis/treatment CAs for the Massey Cancer Center (MCC) at Virginia Commonwealth University. We constructed diagnosis CAs for all cancers based on Virginia state cancer registry data and Bayesian hierarchical logistic regression models. We constructed a diagnosis/treatment CA using billing data from MCC and a Bayesian hierarchical Poisson regression model. To define CAs, we used exceedance probabilities for county random effects to assess unusual spatial clustering of patients diagnosed or treated at MCC after adjusting for important demographic covariates. We used the MCC CAs to compare patient characteristics inside and outside the CAs. Among cancer patients living within the MCC CA, patients diagnosed at MCC were more likely to be minority, female, uninsured, or on Medicaid.
机译:服务区(CA)是癌症中心从中吸引患者的地理区域和人口。定义CA可使癌症中心描述其主要患者人群并评估其在CA中满足癌症患者需求的程度。申请国家癌症研究所(NCI)指定的癌症中心资格的癌症中心需要CA定义。在这项研究中,我们为弗吉尼亚联邦大学梅西癌症中心(MCC)构建了诊断和诊断/治疗CA。我们基于弗吉尼亚州癌症登记数据和贝叶斯分层逻辑回归模型构建了所有癌症的诊断CA。我们使用来自MCC的帐单数据和贝叶斯分层Poisson回归模型构建了诊断/治疗CA。为了定义CA,我们在对重要的人口统计学协变量进行了调整之后,使用了县随机效应的超出概率来评估在MCC诊断或治疗的患者的异常空间聚集。我们使用MCC CA来比较CA内外的患者特征。在生活在MCC CA中的癌症患者中,被诊断为MCC的患者更有可能是少数民族,女性,无保险或享受医疗补助。

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