Literature DB >> 20234799

Cluster morphology analysis.

Geoffrey M Jacquez1.   

Abstract

Most disease clustering methods assume specific shapes and do not evaluate statistical power using the applicable geography, at-risk population, and covariates. Cluster Morphology Analysis (CMA) conducts power analyses of alternative techniques assuming clusters of different relative risks and shapes. Results are ranked by statistical power and false positives, under the rationale that surveillance should (1) find true clusters while (2) avoiding false clusters. CMA then synthesizes results of the most powerful methods. CMA was evaluated in simulation studies and applied to pancreatic cancer mortality in Michigan, and finds clusters of flexible shape while routinely evaluating statistical power.

Entities:  

Keywords:  Clustering methods; medical geography; meta-analysis; statistical power

Mesh:

Year:  2009        PMID: 20234799      PMCID: PMC2838429          DOI: 10.1016/j.sste.2009.08.002

Source DB:  PubMed          Journal:  Spat Spatiotemporal Epidemiol        ISSN: 1877-5845


  43 in total

1.  Guidelines for investigating clusters of health events.

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Journal:  MMWR Recomm Rep       Date:  1990-07-27

2.  An elliptic spatial scan statistic.

Authors:  Martin Kulldorff; Lan Huang; Linda Pickle; Luiz Duczmal
Journal:  Stat Med       Date:  2006-11-30       Impact factor: 2.373

3.  Monitoring for clusters of disease: application to leukemia incidence in upstate New York.

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Journal:  Am J Epidemiol       Date:  1990-07       Impact factor: 4.897

4.  Breast cancer clusters in the northeast United States: a geographic analysis.

Authors:  M Kulldorff; E J Feuer; B A Miller; L S Freedman
Journal:  Am J Epidemiol       Date:  1997-07-15       Impact factor: 4.897

5.  Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico.

Authors:  M Kulldorff; W F Athas; E J Feurer; B A Miller; C R Key
Journal:  Am J Public Health       Date:  1998-09       Impact factor: 9.308

6.  Brain cancer mortality in the United States, 1986 to 1995: a geographic analysis.

Authors:  Zixing Fang; Martin Kulldorff; David I Gregorio
Journal:  Neuro Oncol       Date:  2004-07       Impact factor: 12.300

7.  Exploring associations between residential location and breast cancer incidence in a case-control study.

Authors:  Christopher Paulu; Ann Aschengrau; David Ozonoff
Journal:  Environ Health Perspect       Date:  2002-05       Impact factor: 9.031

8.  A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996-2003.

Authors:  David C Wheeler
Journal:  Int J Health Geogr       Date:  2007-03-27       Impact factor: 3.918

9.  Current practices in cancer spatial data analysis: a call for guidance.

Authors:  Linda Williams Pickle; Lance A Waller; Andrew B Lawson
Journal:  Int J Health Geogr       Date:  2005-01-13       Impact factor: 3.918

10.  Effects of study area size on geographic characterizations of health events: prostate cancer incidence in Southern New England, USA, 1994-1998.

Authors:  David I Gregorio; Holly Samociuk; Laurie DeChello; Helen Swede
Journal:  Int J Health Geogr       Date:  2006-02-15       Impact factor: 3.918

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  12 in total

1.  Performance of cancer cluster Q-statistics for case-control residential histories.

Authors:  Chantel D Sloan; Geoffrey M Jacquez; Carolyn M Gallagher; Mary H Ward; Ole Raaschou-Nielsen; Rikke Baastrup Nordsborg; Jaymie R Meliker
Journal:  Spat Spatiotemporal Epidemiol       Date:  2012-09-24

2.  On the effect of diagnostic misclassification bias on the observed spatial pattern in regional count data--a case study using West Nile virus mortality data from Ontario, 2005.

Authors:  Olaf Berke; Lance Waller
Journal:  Spat Spatiotemporal Epidemiol       Date:  2010-03-20

3.  Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome.

Authors:  Geoffrey M Jacquez; Clive E Sabel; Chen Shi
Journal:  Ann Assoc Am Geogr       Date:  2015

4.  Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment.

Authors:  Aline Guttmann; Xinran Li; Fabien Feschet; Jean Gaudart; Jacques Demongeot; Jean-Yves Boire; Lemlih Ouchchane
Journal:  PLoS One       Date:  2015-06-18       Impact factor: 3.240

5.  Detecting cancer clusters in a regional population with local cluster tests and Bayesian smoothing methods: a simulation study.

Authors:  Dorothea Lemke; Volkmar Mattauch; Oliver Heidinger; Edzer Pebesma; Hans-Werner Hense
Journal:  Int J Health Geogr       Date:  2013-12-07       Impact factor: 3.918

6.  Performance map of a cluster detection test using extended power.

Authors:  Aline Guttmann; Lemlih Ouchchane; Xinran Li; Isabelle Perthus; Jean Gaudart; Jacques Demongeot; Jean-Yves Boire
Journal:  Int J Health Geogr       Date:  2013-10-25       Impact factor: 3.918

7.  Spatial heterogeneity of type I error for local cluster detection tests.

Authors:  Aline Guttmann; Xinran Li; Jean Gaudart; Yan Gérard; Jacques Demongeot; Jean-Yves Boire; Lemlih Ouchchane
Journal:  Int J Health Geogr       Date:  2014-05-27       Impact factor: 3.918

8.  Selection of the Maximum Spatial Cluster Size of the Spatial Scan Statistic by Using the Maximum Clustering Set-Proportion Statistic.

Authors:  Yue Ma; Fei Yin; Tao Zhang; Xiaohua Andrew Zhou; Xiaosong Li
Journal:  PLoS One       Date:  2016-01-28       Impact factor: 3.240

9.  Geographic disparities and temporal changes in risk of death from myocardial infarction in Florida, 2000-2014.

Authors:  Evah W Odoi; Nicholas Nagle; Shamarial Roberson; Kristina W Kintziger
Journal:  BMC Public Health       Date:  2019-05-03       Impact factor: 3.295

10.  Disparities in Temporal and Geographic Patterns of Myocardial Infarction Hospitalization Risks in Florida.

Authors:  Evah W Odoi; Nicholas Nagle; Chris DuClos; Kristina W Kintziger
Journal:  Int J Environ Res Public Health       Date:  2019-11-27       Impact factor: 3.390

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