Temporal and spatial distribution of human cryptosporidiosis in the west of Ireland 2004-2007.
Authors
Callaghan, MaryCormican, Martin
Prendergast, Martina
Pelly, Heidi
Cloughley, Richard
Hanahoe, Belinda
O'Donovan, Diarmuid
Affiliation
Environmental Change Institute, National University of Ireland, Galway, Ireland. mary.callaghan@nuigalway.ieIssue Date
2009MeSH
Cluster AnalysisCryptosporidiosis
Disease Outbreaks
Geography
Humans
Ireland
Population Surveillance
Time Factors
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Temporal and spatial distribution of human cryptosporidiosis in the west of Ireland 2004-2007. 2009, 8:64 Int J Health GeogrJournal
International journal of health geographicsDOI
10.1186/1476-072X-8-64PubMed ID
19930685Abstract
BACKGROUND: Cryptosporidiosis is increasingly recognised as a cause of gastrointestinal infection in Ireland and has been implicated in several outbreaks. This study aimed to investigate the spatial and temporal distribution of human cryptosporidiosis in the west of Ireland in order to identify high risk seasons and areas and to compare Classically Calculated (CC) and Empirical Bayesian (EB) incidence rates. Two spatial scales of analysis were used with a view to identifying the best one in assessing geographical patterns of infection. Global Moran's I and Local Moran's I tests of autocorrelation were used to test for evidence of global and local spatial clustering. RESULTS: There were statistically significant seasonal patterns of cryptosporidiosis with peaks in spring and an increasing temporal trend. Significant (p < 0.05) global spatial clustering was observed in CC rates at the Electoral Division (ED) level but not in EB rates at the same level. Despite variations in disease, ED level was found to provide the most accurate account of distribution of cryptosporidiosis in the West of Ireland but required spatial EB smoothing of cases. There were a number of areas identified with significant local clustering of cryptosporidiosis rates. CONCLUSION: This study identified spatial and temporal patterns in cryptosporidiosis distribution. The study also showed benefit in performing spatial analyses at more than one spatial scale to assess geographical patterns in disease distribution and that smoothing of disease rates for mapping in small areas enhances visualisation of spatial patterns. These findings are relevant in guiding policy decisions on disease control strategies.Language
enISSN
1476-072Xae974a485f413a2113503eed53cd6c53
10.1186/1476-072X-8-64
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