Zobrazeno 1 - 10
of 806
pro vyhledávání: '"Kulldorff Martin"'
Publikováno v:
International Journal of Health Geographics, Vol 9, Iss 1, p 61 (2010)
Abstract Background The spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically signi
Externí odkaz:
https://doaj.org/article/adfedde0894445489b19606d67a825ee
Publikováno v:
International Journal of Health Geographics, Vol 8, Iss 1, p 58 (2009)
Abstract Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of th
Externí odkaz:
https://doaj.org/article/7c1544809977487f94b2cbd1d3f87884
Publikováno v:
International Journal of Health Geographics, Vol 8, Iss 1, p 41 (2009)
Abstract Background Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real u
Externí odkaz:
https://doaj.org/article/d1ee3a6988594272847b505c064ec960
Publikováno v:
International Journal of Health Geographics, Vol 7, Iss 1, p 14 (2008)
Abstract Background Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindric
Externí odkaz:
https://doaj.org/article/422596a3913441dea0d358ac955d4fbe
Publikováno v:
International Journal of Health Geographics, Vol 6, Iss 1, p 14 (2007)
Abstract Background With the objective of identifying spatial and temporal patterns of enzootic raccoon variant rabies, a spatial scan statistic was utilized to search for significant terrestrial rabies clusters by year in New York State in 1997–20
Externí odkaz:
https://doaj.org/article/623a5723c35949c6bdcb7313ff1773eb
Autor:
Kulldorff Martin, Song Changhong
Publikováno v:
International Journal of Health Geographics, Vol 4, Iss 1, p 32 (2005)
Abstract Background Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting
Externí odkaz:
https://doaj.org/article/77c413aaa2744ec4a2a1fea5ccfcba38
Publikováno v:
International Journal of Health Geographics, Vol 4, Iss 1, p 6 (2005)
Abstract Background Findings are compared on geographic variation of incident and late-stage cancers across Connecticut using different areal units for analysis. Results Few differences in results were found for analyses across areal units. Global cl
Externí odkaz:
https://doaj.org/article/5cf933ae175f488fa883bf4479b2c5c8
Publikováno v:
International Journal of Health Geographics, Vol 4, Iss 1, p 1 (2005)
Abstract Background Spatial variation in patterns of disease outcomes is often explored with techniques such as cluster detection analysis. In other types of investigations, geographically varying individual or community level characteristics are oft
Externí odkaz:
https://doaj.org/article/3fd91e684a8d479b9470f8b78c6436c7
Autor:
Gershman Susan, Gregorio David I, Kulldorff Martin, DeChello Laurie M, Joseph Sheehan T, Mroszczyk Mary
Publikováno v:
International Journal of Health Geographics, Vol 3, Iss 1, p 17 (2004)
Abstract Background The aims of this study were to determine whether observed geographic variations in breast cancer incidence are random or statistically significant, whether statistically significant excesses are temporary or time-persistent, and w
Externí odkaz:
https://doaj.org/article/c0725a0bbd8e493b87cdc664b42c3f71
Autor:
Kulldorff Martin, Song Changhong
Publikováno v:
International Journal of Health Geographics, Vol 2, Iss 1, p 9 (2003)
Abstract Background Many different test statistics have been proposed to test for spatial clustering. Some of these statistics have been widely used in various applications. In this paper, we use an existing collection of 1,220,000 simulated benchmar
Externí odkaz:
https://doaj.org/article/fdeab3d45eca4fde9777f6dc196b504b