A scan statistic for continuous data based on the normal probability model.
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 those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. martin_kulldorff@hms.harvard.edu
Martin
M
Kulldorff
Lan
L
Huang
Kevin
K
Konty
D016000:Cluster Analysis; D040362:Geographic Information Systems; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D015233:Models, Statistical; D009010:Monte Carlo Method; D009519:New York City / Q000453:epidemiology; D015995:Prevalence; D016016:Proportional Hazards Models; D012984:Software