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Spatio-temporal models to determine association between Campylobacter cases and environment

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Abstract

Background: Campylobacteriosis is a major cause of gastroenteritis in the UK, and although 70% of cases are associated with food sources, the remainder are probably associated with wider environmental exposure.

Methods: In order to investigate wider environmental transmission, we conducted a spatio-temporal analysis of the association of human cases of Campylobacter in the Tyne catchment with weather, climate, hydrology and land use. A hydrological model was used to predict surface-water flow in the Tyne catchment over 5 years. We analysed associations between population-adjusted Campylobacter case rate and environmental factors hypothesised to be important in disease using a two stage modelling framework. First, we investigated associations between temporal variation in case rate in relation to surface-water flow, temperature, evapotranspiration and rainfall using linear mixed-effects models. Second, we used the random effects for the first model to quantify how spatial variation in static landscape features of soil and land use impacted on the likely differences between subcatchment associations of case rate with the temporal variables. 
Results: Population-adjusted Campylobacter case rates were associated with periods of high predicted surface-water flow, and during above average temperatures. Subcatchments with cattle on stagnogley soils, and to a lesser extent sheep plus cattle grazing, had higher Campylobacter case rates. 
Conclusions: Areas of stagnogley soils with mixed livestock grazing may be more vulnerable to both Campylobacter spread and exposure during periods of high rainfall, with resultant increased risk of human cases of the disease.

Details

Original languageEnglish
Pages (from-to)202–216
JournalInternational Journal of Epidemiology
Volume47
Issue number1
Early online date24 Oct 2017
DOIs
StatePublished - 1 Feb 2018
Peer-reviewedYes

Keywords

    Research areas

  • Campylobacter, hydrology, livestock, rainfall, soils

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