Catchment Area Analysis Using Bayesian Regression Modeling
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 cente...
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Online Access: | https://doi.org/10.4137/CIN.S17297 |
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doaj-71ff0bb7b0244006a5d4ec6b3643fb432020-11-25T03:45:23ZengSAGE PublishingCancer Informatics1176-93512015-01-0114s210.4137/CIN.S17297Catchment Area Analysis Using Bayesian Regression ModelingAobo Wang0David C. Wheeler1Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.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.https://doi.org/10.4137/CIN.S17297 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aobo Wang David C. Wheeler |
spellingShingle |
Aobo Wang David C. Wheeler Catchment Area Analysis Using Bayesian Regression Modeling Cancer Informatics |
author_facet |
Aobo Wang David C. Wheeler |
author_sort |
Aobo Wang |
title |
Catchment Area Analysis Using Bayesian Regression Modeling |
title_short |
Catchment Area Analysis Using Bayesian Regression Modeling |
title_full |
Catchment Area Analysis Using Bayesian Regression Modeling |
title_fullStr |
Catchment Area Analysis Using Bayesian Regression Modeling |
title_full_unstemmed |
Catchment Area Analysis Using Bayesian Regression Modeling |
title_sort |
catchment area analysis using bayesian regression modeling |
publisher |
SAGE Publishing |
series |
Cancer Informatics |
issn |
1176-9351 |
publishDate |
2015-01-01 |
description |
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. |
url |
https://doi.org/10.4137/CIN.S17297 |
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