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    Temporal and spatial distribution of human cryptosporidiosis in the west of Ireland 2004-2007.

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    Authors
    Callaghan, Mary
    Cormican, 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.ie
    Issue Date
    2009
    MeSH
    Cluster Analysis
    Cryptosporidiosis
    Disease Outbreaks
    Geography
    Humans
    Ireland
    Population Surveillance
    Time Factors
    
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    Citation
    Temporal and spatial distribution of human cryptosporidiosis in the west of Ireland 2004-2007. 2009, 8:64 Int J Health Geogr
    Journal
    International journal of health geographics
    URI
    http://hdl.handle.net/10147/95274
    DOI
    10.1186/1476-072X-8-64
    PubMed ID
    19930685
    Abstract
    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
    en
    ISSN
    1476-072X
    ae974a485f413a2113503eed53cd6c53
    10.1186/1476-072X-8-64
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