Baylisascaris procyonis, Raccoon Roundworm

Classification Raccoon Roundworm KingdomAnimalia PhylumNemathelminthes ClassNematoda OrderAscaridida FamilyAscarididae GenusBaylisascaris Speciesprocyonis

The Smithsonian Book of North American Mammals edited by Don E. Wilson and Sue Ruff. North American Distribution Small Dark Large Light colour

Procyon lotor

Hatched, stained, nonviable Baylisascaris procyonis larvae (magnification ×10). Source:Shafir S, Wang W, Sorvillo F, Wise M, Moore L, Sorvillo T, Eberhard M. "Thermal Death Point of Baylisascaris procyonis Eggs". Emerg Infect Dis [serial on the Internet] Jan [cited 2007 Feb 23]. Available from procyonishttp://

Proven human cases have been reported in California, Oregon, New York, Pennsylvania, Illinois, Michigan, and Minnesota, with a suspected case in Missouri.

latrines

Study Objectives  Using data routinely collected through Winnipeg’s urban raccoon complaint and control program, estimate the geographic distribution of the raccoon population in Winnipeg model against ecological predictors  Habitat (rivers streams), abandoned houses, or houses in disrepair  Potential food sources (no of people, no. of restaurants, garbage disposal type (dumpster vs. non-dumpster neighborhoods)

Study Objectives cont’d  Assess temporal and spatial variations in B. procyonis infection in urban racoons Testing of racoon feces obtained at 52 designated latrine sites Necroptic analysis of raccoon specimens supplied by Manitoba Conservation

Study Objectives cont’d  Assess potential risk to the human population (primarily children) of exposure to raccoon carried B. procyonis through geographic proximity analysis Identify geographic areas having high raccoon densities and a high concentration of young children Identify schools, daycares, parks, community centres and other relevant facilities in high risk areas

Study Protocol  Summer Student was hired to collect and enter data Raccoon complaint data from 2003 to 2007 were entered into a database and mapped (n=1119) Latrine samples were collected and tested over the summer of 2007 (52 latrines, 2 samples per latrine). Raccoon necropsies (n=114) were collected over the summer of 2007

Raccoon Complaints by year

Analytical Approaches  Geographic Mapping (visualization) Surface analysis  Raccoon complaints per square km Chloropleth mapping  Raccoon complaints /1000 human population by neighborhood, smoothed  Cluster Analysis (Satscan) Identification of statistically significant clusters  raccoon complaints / human population  BP infected specimens / total specimens  Temporal Analysis Monthly variation in BP infection of specimens  Poisson Regression Analysis Modeling raccoon complaints / human population by demographic and landscape predictors

Results  Geographic Mapping Arc-GIS Google Earth  Cluster Analysis Sign. Clusters of Raccoon Complaints\ No sign. Clusters of BP infected specimens  Temporal Analysis Only random temporal variation

Toblers Law: Things that are closer together are more similar than things farther apart. Numerator and denominator data from the neighbors of each geographic area are aggregated in order to create a smoothed rate estimate for each geographic area. The smoothed rate is an attempt to estimate what the rate would be if there were sufficient population and sufficient time for the underlying risk processes to manifest themselves as a stable rate. Smoothing Calculation: Numerator: = 65 Denominator: = 6500 Smoothed Rate: 65/6500 = 10/1000 compares to: Crude Rate: 3/500 = 6/1000 3/500 12/1000 4/20020/3000 6/900 20/900 Spatial Smoothing can be implemented using a user written program, or with products such as GeoDA, or Space Stat Dealing with Unstable Rates Spatial Smoothing

Spatial Scan Statistic Are Observed Spatial Patterns Random or Real? The Spatial Scan Statistic applies to the centroid of each geographic area a set of ever-expanding concentric circles. As the circle expands and begins to encompass the centroids of neighboring geographic areas, new rates are calculated. When sets of contiguous geographic areas are found which appear to have rates significantly higher or lower than expected, these rates are tested through a Monte Carlo simulation. Monte Carlo simulation randomly places the data on the map 1000 times in order to determine how unusual the observed rate is compared to a random world. The result is the identification of a set of geographic clusters having: a. Numerators and Denominators of sufficient size to be considered stable b. Rates which are unlikely to have occurred by chance alone The Spatial Scan is Implemented using Satscan, an open domain product from the National Cancer Institute in the U.S.