Literature DB >> 17179759

Detection of spatial clusters: application to cancer survival as a continuous outcome.

Lan Huang1, Linda W Pickle, David Stinchcomb, Eric J Feuer.   

Abstract

In this article, we develop the first detailed illustration of the use of a cluster detection method using a spatial scan statistic based on an exponential survival model. We use this approach to study the spatial patterns of survival of patients with stage III or stage IV colorectal cancer or with stage I/II, stage III, or stage IV lung cancer in the State of California and the County of Los Angeles (LA) diagnosed during 1988 through 2002. We present the location of the detected clusters of short survival or long survival and compute nonparametric estimates of survival inside and outside of those detected clusters confirming the survival pattern detected by the spatial scan statistic in both areas. In LA County, we investigate the possible relationship between the cluster locations and race, sex, and histology using nonparametric methods, and we compare socioeconomic factors such as education, employment, income, and health insurance inside and outside of the detected clusters. Finally, we evaluate the effect of related covariates on statistically significant long and short survival clusters detected in LA County using logistic regression models. This article illustrates a new way to understand survival patterns that may point to health disparities in terms of diagnosis and treatment patterns.

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Year:  2007        PMID: 17179759     DOI: 10.1097/01.ede.0000249994.30736.24

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  9 in total

Review 1.  A review of spatial methods in epidemiology, 2000-2010.

Authors:  Amy H Auchincloss; Samson Y Gebreab; Christina Mair; Ana V Diez Roux
Journal:  Annu Rev Public Health       Date:  2012-04       Impact factor: 21.981

2.  Temporal trends in geographic disparities in small-area-level colorectal cancer incidence and mortality in the United States.

Authors:  Mario Schootman; Min Lian; Anjali D Deshpande; Amy McQueen; Sandi L Pruitt; Donna B Jeffe
Journal:  Cancer Causes Control       Date:  2011-06-19       Impact factor: 2.506

3.  The role of poverty rate and racial distribution in the geographic clustering of breast cancer survival among older women: a geographic and multilevel analysis.

Authors:  Mario Schootman; Donna B Jeffe; Min Lian; William E Gillanders; Rebecca Aft
Journal:  Am J Epidemiol       Date:  2008-12-22       Impact factor: 4.897

4.  Differences in Travel Time to Cancer Surgery for Colon versus Rectal Cancer in a Rural State: A New Method for Analyzing Time-to-Place Data Using Survival Analysis.

Authors:  Kevin A Matthews; Amanda R Kahl; Anne H Gaglioti; Mary E Charlton
Journal:  J Rural Health       Date:  2020-06-05       Impact factor: 5.667

5.  Spatial Analysis of Factors Associated with Household Subscription to the National Health Insurance Scheme in Rural Ghana.

Authors:  Stephen Manortey; James VanDerslice; Steve Alder; Kevin A Henry; Benjamin Crookston; Ty Dickerson; Scott Benson
Journal:  J Public Health Afr       Date:  2014-02-04

6.  Socioeconomic Status and Distance to Reference Centers for Complex Cancer Diseases: A Source of Health Inequalities? A Population Cohort Study Based on Catalonia (Spain).

Authors:  Paula Manchon-Walsh; Luisa Aliste; Josep M Borràs; Cristina Coll-Ortega; Joan Casacuberta; Cristina Casanovas-Guitart; Montse Clèries; Sergi Cruz; Àlex Guarga; Anna Mompart; Antoni Planella; Alfonso Pozuelo; Isabel Ticó; Emili Vela; Joan Prades
Journal:  Int J Environ Res Public Health       Date:  2022-07-20       Impact factor: 4.614

7.  Geographic disparities in colorectal cancer survival.

Authors:  Kevin A Henry; Xiaoling Niu; Francis P Boscoe
Journal:  Int J Health Geogr       Date:  2009-07-23       Impact factor: 3.918

8.  Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.

Authors:  Sujee Lee; Jisu Moon; Inkyung Jung
Journal:  Int J Health Geogr       Date:  2021-07-08       Impact factor: 3.918

9.  Estimating the accuracy of geographical imputation.

Authors:  Kevin A Henry; Francis P Boscoe
Journal:  Int J Health Geogr       Date:  2008-01-23       Impact factor: 3.918

  9 in total

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