American Journal of Epidemiology Advance Access originally published online on November 23, 2005
American Journal of Epidemiology 2006 163(2):171-180; doi:10.1093/aje/kwj023
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Practice of Epidemiology |
Geographic Prediction of Human Onset of West Nile Virus Using Dead Crow Clusters: An Evaluation of Year 2002 Data in New York State
1 Zoonoses Program, New York State Department of Health, Albany, NY
2 Department of Environmental Health Sciences, School of Public Health, University at Albany, Albany, NY
3 Department of Epidemiology, School of Public Health, University at Albany, Albany, NY
4 Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA
Correspondence to Dr. Millicent Eidson, Zoonoses Program, New York State Department of Health, 621 Corning Tower, Empire State Plaza, Albany, NY 12237 (e-mail: mxe04{at}health.state.ny.us).
The risk of becoming a West Nile virus case in New York State, excluding New York City, was evaluated for persons whose town of residence was proximal to spatial clusters of dead American crows (Corvus brachyrhynchos). Weekly clusters were delineated for June–October 2002 by using both the binomial spatial scan statistic and kernel density smoothing. The relative risk of a human case was estimated for different spatial-temporal exposure definitions after adjusting for population density and age distribution using Poisson regression, adjusting for week and geographic region, and conducting Cox proportional hazards modeling, where the week that a human case was identified was treated as the failure time and baseline hazard was stratified by region. The risk of becoming a West Nile virus case was positively associated with living in towns proximal to dead crow clusters. The highest risk was consistently for towns associated with a cluster in the current or prior 1–2 weeks. Weaker, but positive associations were found for towns associated with a cluster in just the 1–2 prior weeks, indicating an ability to predict onset in a timely fashion.
arboviruses; geographic information systems; Poisson distribution; population surveillance; proportional hazards models; space-time clustering; West Nile virus; zoonoses
Abbreviations: WNV, West Nile virus
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