Geographical Differences in Primary Therapy for Early-Stage Breast Cancer

Gregorio, David I.; Kulldorff, Martin; Barry, Leah; Samocuik, Holly; Zarfos, K

doi:10.1007/s10434-001-0844-4

Cited by 26 publications

(3 citation statements)

References 22 publications

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“…Many prior studies of geographic variation of disease describe only the area with increased rates or risk without looking at the population characteristics within the identified areas [7,9,11,41]. In this study, we described the sociodemographic factors of the persons diagnosed with colorectal cancer in areas with more or less late stage disease.…”

Section: Inability To Adjust For Covariatesmentioning

confidence: 94%

Use of the Spatial Scan Statistic to Identify Geographic Variations in Late Stage Colorectal Cancer in California (United States)

Pollack

Gotway

Bates

et al. 2006

Cancer Causes Control

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Section: Inability To Adjust For Covariatesmentioning

confidence: 94%

Use of the Spatial Scan Statistic to Identify Geographic Variations in Late Stage Colorectal Cancer in California (United States)

Pollack

Gotway

Bates

et al. 2006

Cancer Causes Control

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“…Once identified, these methods can be standardized to improve consistency across many locations, in order to gain effectiveness. Further granularity can be provided to allow for reduced area identification, in which specific disease control strategies can be implemented to supplement broader programs (Gregorio, Kulldorff, Barry, Samocuik, & Zarfos, 2001).…”

Section: Practitioner Implicationsmentioning

confidence: 99%

Space-Time Cluster Analysis

Woodside

Sikder

2009

International Journal of Healthcare Information Systems and Informatics

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Spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decision making. This article illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization (Kaiser Foundation Health Plan of Ohio) over a period 1994—2006. The objective is to find excess space and space - time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski, & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.

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“…By contrast, Bernoulli models are used for dichotomous variables, such as early/late disease stage, prevalence and treatment data. One example is the study of geographical differences in primary therapy for early-stage breast cancer in Connecticut (Gregorio et al, 2001) and another is the spatial variation of late detection of breast and colorectal cancer in Minnesota (Thomas and Carlin, 2003).…”

Section: Introductionmentioning

confidence: 99%

A Spatial Scan Statistic for Survival Data

2007

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Spatial scan statistics with Bernoulli and Poisson models are commonly used for geographical disease surveillance and cluster detection. These models, suitable for count data, were not designed for continuous outcome data. We propose a spatial scan statistic based on an exponential model to be used for uncensored or censored continuous survival data. The power and sensitivity of the developed model are investigated through intensive simulations. The method performs well for different survival distribution functions including the exponential, gamma and log normal distributions. How to adjust the analysis for covariates is described in detail. The method is illustrated using survival data for men diagnosed with prostate cancer in Connecticut from 1984 to 1995.

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Geographical Differences in Primary Therapy for Early-Stage Breast Cancer

Abstract: Geographical analysis is a way for physicians and health officials to identify groups of women who may not yet benefit from preferred surgical procedures.

Cited by 26 publications

References 22 publications

Use of the Spatial Scan Statistic to Identify Geographic Variations in Late Stage Colorectal Cancer in California (United States)

Use of the Spatial Scan Statistic to Identify Geographic Variations in Late Stage Colorectal Cancer in California (United States)

Space-Time Cluster Analysis

A Spatial Scan Statistic for Survival Data

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