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Literature DB >> 7701154

Adjusting Moran's I for population density.

Abstract

I derive two new statistics, Ipop and Ipop*, that adjust Moran's I to study clustering of disease cases in areas (for example, counties) with different, known population densities. A simulation of Lyme disease in Georgia suggests that these new statistics can be more powerful than those currently in use. This is because they consider both spatial pattern and non-binomial variance in rates as evidence supporting disease clusters.

Entities: Chemical Disease

Mesh：

Year: 1995 PMID： 7701154 DOI： 10.1002/sim.4780140104

Source DB: PubMed Journal: Stat Med ISSN： 0277-6715 Impact factor: 2.373

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Cited

25 in total

1. Detection of temporal changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models.

Authors: Pierre Goovaerts; Geoffrey M Jacquez
Journal: J Geogr Syst Date: 2005-05

2. Secular trends, race, and geographic disparity of early-stage breast cancer incidence: 25 years of surveillance in Connecticut.

Authors: J Christopher F Crabbe; David I Gregorio; Holly Samociuk; Helen Swede
Journal: Am J Public Health Date: 2015-04-23 Impact factor: 9.308

3. Geostatistical Analysis of County-Level Lung Cancer Mortality Rates in the Southeastern United States.

Authors: Pierre Goovaerts
Journal: Geogr Anal Date: 2010-01-01

4. Cancer map patterns: are they random or not?

Authors: Martin Kulldorff; Changhong Song; David Gregorio; Holly Samociuk; Laurie DeChello
Journal: Am J Prev Med Date: 2006-02 Impact factor: 5.043

5. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand.

Authors: S Thammapalo; V Chongsuvivatwong; A Geater; M Dueravee
Journal: Epidemiol Infect Date: 2007-03-15 Impact factor: 2.451

Adjusting Moran's I for population density.

1. Detection of temporal changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models.

2. Secular trends, race, and geographic disparity of early-stage breast cancer incidence: 25 years of surveillance in Connecticut.

3. Geostatistical Analysis of County-Level Lung Cancer Mortality Rates in the Southeastern United States.

4. Cancer map patterns: are they random or not?

5. Environmental factors and incidence of dengue fever and dengue haemorrhagic fever in an urban area, Southern Thailand.

6. Spatial clusters of cancers in Illinois 1986-2000.

7. A geospatial analysis of persons opting out of an exception from informed consent out-of-hospital clinical trial.

8. Measuring global spatial autocorrelation with data reliability information.

9. Time-space clustering of human brucellosis, California, 1973-1992.

10. Comparison of tests for spatial heterogeneity on data with global clustering patterns and outliers.