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Catchment area analysis using bayesian regression modeling.

Wang A, Wheeler DC - Cancer Inform (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

CA for diagnosis/treatment for MCC based on MCC data in 2009–2012 for all cancers (n = 47 counties).
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Related In: Results  -  Collection


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f4-cin-suppl_2-2015-071: CA for diagnosis/treatment for MCC based on MCC data in 2009–2012 for all cancers (n = 47 counties).

Mentions: The CA for diagnosis/treatment at MCC is shown in Figure 3 for 2009–2011 and in Figure 4 for 2009–2012. There were 44 out of 134 counties in Virginia included in the CA for diagnosis/treatment in 2009–2011, while 47 counties were included in the 2009–2012 CA. As with the diagnosis CA for 2009–2011, the diagnosis/treatment CAs were centered on the city Richmond and were contiguous, with the exception of the inclusion of part of the Eastern Shore in 2009–2012. The CA increased in size by three counties when including data for 2012, suggesting that MCC expanded its service area over time. Specifically, the CA expanded slightly to the east and to the north. The 2009–2012 CA added part of the Eastern Shore to the 2009–2011 CA, and it added a county in northern Virginia. The county membership for all three CAs is listed in Supplementary Table 1.


Catchment area analysis using bayesian regression modeling.

Wang A, Wheeler DC - Cancer Inform (2015)

CA for diagnosis/treatment for MCC based on MCC data in 2009–2012 for all cancers (n = 47 counties).
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4403915&req=5

f4-cin-suppl_2-2015-071: CA for diagnosis/treatment for MCC based on MCC data in 2009–2012 for all cancers (n = 47 counties).
Mentions: The CA for diagnosis/treatment at MCC is shown in Figure 3 for 2009–2011 and in Figure 4 for 2009–2012. There were 44 out of 134 counties in Virginia included in the CA for diagnosis/treatment in 2009–2011, while 47 counties were included in the 2009–2012 CA. As with the diagnosis CA for 2009–2011, the diagnosis/treatment CAs were centered on the city Richmond and were contiguous, with the exception of the inclusion of part of the Eastern Shore in 2009–2012. The CA increased in size by three counties when including data for 2012, suggesting that MCC expanded its service area over time. Specifically, the CA expanded slightly to the east and to the north. The 2009–2012 CA added part of the Eastern Shore to the 2009–2011 CA, and it added a county in northern Virginia. The county membership for all three CAs is listed in Supplementary Table 1.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus