International Journal of Health Geographics | 2010
Authors:
DOI: 10.1186/1476-072X-9-31
Journal: International Journal of Health Geographics
Year: 2010
Publisher:
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 7
Background: Cryptosporidium parvum is one of the most important biological contaminants in drinking water that produces life threatening infection in people with compromised immune systems. Dairy calves are thought to be the primary source of C. parvum contamination in watersheds. Understanding the spatial and temporal variation in the risk of C. parvum infection in dairy cattle is essential for designing cost-effective watershed management strategies to protect drinking water sources. Crude and Bayesian seasonal risk estimates for Cryptosporidium in dairy calves were used to investigate the spatio-temporal dynamics of C. parvum infection on dairy farms in the New York City watershed.Results: Both global (Global Moran's I) and specific (SaTScan) cluster analysis methods revealed a significant (p < 0.05) elliptical spatial cluster in the winter with a relative risk of 5.8, but not in other seasons. There was a two-fold increase in the risk of C. parvum infection in all herds in the summer (p = 0.002), compared to the rest of the year. Bayesian estimates did not show significant spatial autocorrelation in any season.Conclusions: Although we were not able to identify seasonal clusters using Bayesian approach, crude estimates highlighted both temporal and spatial clusters of C. parvum infection in dairy herds in a major watershed. We recommend that further studies focus on the factors that may lead to the presence of C. parvum clusters within the watershed, so that monitoring and prevention practices such as stream monitoring, riparian buffers, fencing and manure management can be prioritized and improved, to protect drinking water supplies and public health. © 2010 Szonyi et al; licensee BioMed Central Ltd.
Animal Husbandry; Animals; Bayes Theorem; Cattle; Cattle Diseases; Cluster Analysis; Cross-Sectional Studies; Cryptosporidiosis; Cryptosporidium parvum; Dairying; Incidence; New York City; Risk Assessment; Water Microbiology; Water Supply; New York [New York (STT)]; New York [United States]; United States; Bos; Cryptosporidium; Cryptosporidium parvum; autocorrelation; Bayesian analysis; cluster analysis; dairy farming; immune system; infectious disease; management practice; parasitic disease; public health; risk assessment; spatial variation; temporal variation; water supply; animal; animal disease; animal husbandry; article; Bayes theorem; cattle; cattle disease; cluster analysis; cross-sectional study; cryptosporidiosis; Cryptosporidium parvum; dairying; disease transmission; incidence; isolation and purification; microbiology; parasitology; risk assessment; United States; water supply