Abstract
The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits.
| Original language | English |
|---|---|
| Pages (from-to) | 5106-5118 |
| Number of pages | 13 |
| Journal | Statistics in Medicine |
| Volume | 32 |
| Issue number | 29 |
| DOIs | |
| Publication status | Published - 2013 Dec 20 |
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All Science Journal Classification (ASJC) codes
- Epidemiology
- Statistics and Probability
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A spatial scan statistic for compound Poisson data. / Rosychuk, Rhonda J.; Chang, Hsing Ming.
In: Statistics in Medicine, Vol. 32, No. 29, 20.12.2013, p. 5106-5118.Research output: Contribution to journal › Article
TY - JOUR
T1 - A spatial scan statistic for compound Poisson data
AU - Rosychuk, Rhonda J.
AU - Chang, Hsing Ming
PY - 2013/12/20
Y1 - 2013/12/20
N2 - The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits.
AB - The topic of spatial cluster detection gained attention in statistics during the late 1980s and early 1990s. Effort has been devoted to the development of methods for detecting spatial clustering of cases and events in the biological sciences, astronomy and epidemiology. More recently, research has examined detecting clusters of correlated count data associated with health conditions of individuals. Such a method allows researchers to examine spatial relationships of disease-related events rather than just incident or prevalent cases. We introduce a spatial scan test that identifies clusters of events in a study region. Because an individual case may have multiple (repeated) events, we base the test on a compound Poisson model. We illustrate our method for cluster detection on emergency department visits, where individuals may make multiple disease-related visits.
UR - http://www.scopus.com/inward/record.url?scp=84887260885&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887260885&partnerID=8YFLogxK
U2 - 10.1002/sim.5891
DO - 10.1002/sim.5891
M3 - Article
C2 - 23824973
AN - SCOPUS:84887260885
VL - 32
SP - 5106
EP - 5118
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 29
ER -