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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 2009–2011 for all cancers using the local spatial scan (n = 41 counties).
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f8-cin-suppl_2-2015-071: CA for diagnosis/treatment for MCC based on MCC data in 2009–2011 for all cancers using the local spatial scan (n = 41 counties).

Mentions: The diagnosis CA maps based on K-means clustering and the local spatial scan are shown in Figures 5 and 6. The number of counties included in the CAs was 30 for K-means clustering and 42 for the local spatial scan. The diagnosis/treatment CA maps based on K-means clustering and the local spatial scan are shown in Figures 7 and 8. The number of counties included in the CAs was 33 for K-means clustering and 41 for the local spatial scan. Compared with our Bayesian model diagnosis and diagnosis/treatment CAs, which contained 54 and 44 counties, respectively, we see that K-means clustering and the local spatial scan yielded smaller CAs. The CAs overlap spatially to the extent that the Bayesian CAs effectively contain not only the CAs estimated from K-means clustering and the local spatial scan but also adjacent counties.


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 2009–2011 for all cancers using the local spatial scan (n = 41 counties).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f8-cin-suppl_2-2015-071: CA for diagnosis/treatment for MCC based on MCC data in 2009–2011 for all cancers using the local spatial scan (n = 41 counties).
Mentions: The diagnosis CA maps based on K-means clustering and the local spatial scan are shown in Figures 5 and 6. The number of counties included in the CAs was 30 for K-means clustering and 42 for the local spatial scan. The diagnosis/treatment CA maps based on K-means clustering and the local spatial scan are shown in Figures 7 and 8. The number of counties included in the CAs was 33 for K-means clustering and 41 for the local spatial scan. Compared with our Bayesian model diagnosis and diagnosis/treatment CAs, which contained 54 and 44 counties, respectively, we see that K-means clustering and the local spatial scan yielded smaller CAs. The CAs overlap spatially to the extent that the Bayesian CAs effectively contain not only the CAs estimated from K-means clustering and the local spatial scan but also adjacent counties.

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