Investigation of clusters of giardiasis using GIS and a spatial scan statistic

BACKGROUND: Giardia lamblia is the most frequently identified human intestinal parasite in Canada with prevalence estimates of 4-10%. However, infection rates vary by geographical area and localized 'pockets' of high or low infection rates are thought to exist. Water-borne transmission is one of the major routes of infection. Sources of contamination of drinking water include humans, domestic and wild animals. A previous study in southern Ontario, Canada, indicated a bivariate association between giardiasis rates and livestock density and/or manure use on agricultural land; however these variables were not significant when the variable 'rural' was added to the model. In that study, urban areas were defined as those with a minimum of 1,000 persons and a population density of at least 400 persons per Km2; all other areas were considered rural. This paper investigates the presence of local giardiasis clusters and considers the extent to which livestock density and manure application on agricultural land might explain the 'rural' effect. A spatial scan statistic was used to identify spatial clusters and geographical correlation analysis was used to explore associations of giardiasis rates with manure application on agricultural land and livestock density. RESULTS: Significant (P < 0.05) high rate spatial clusters were identified in a number of areas. Results also showed significant (P < 0.05) associations between giardiasis rates and both livestock density and manure application on agricultural land. However, the associations were observed in only two regions. CONCLUSIONS: There is evidence that giardiasis clusters in space in southern Ontario. However, there is no strong evidence to suggest that either livestock density or manure application on agricultural land plays an important role in the epidemiology of giardiasis in the study area. Therefore these factors do not seem to explain the higher rates of giardiasis reported in rural areas. The spatial scan statistics methodology used in this study has an important potential use in disease surveillance for confirming or refuting cluster alarms.