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Background  The rate of community-acquired Clostridium difficile infection (CA-CDI) is increasing. While receipt of antibiotics remains an important risk factor for CDI, studies related to acquisition of C. difficile outside of hospitals are lacking. As a result, risk factors for exposure to  C .  difficile  in community settings have been inadequately studied.    Main objective  To identify novel environmental risk factors for CA-CDI    Methods  We performed a population-based retrospective cohort study of patients with CA-CDI from 1/1/2007 through 12/31/2014 in a 10-county area in central North Carolina. 360 Census Tracts in these 10 counties were used as the demographic Geographic Information System (GIS) base-map. Longitude and latitude (X, Y) coordinates were generated from patient home addresses and overlaid to Census Tracts polygons using ArcGIS; ArcView was used to assess “hot-spots” or clusters of CA-CDI. We then constructed a mixed hierarchical model to identify environmental variables independently associated with increased rates of CA-CDI.    Results  A total of 1,895 unique patients met our criteria for CA-CDI. The mean patient age was 54.5 years; 62% were female and 70% were Caucasian. 402 (21%) patient addresses were located in “hot spots” or clusters of CA-CDI (p<0.001). “Hot spot” census tracts were scattered throughout the 10 counties. After adjusting for clustering and population density, age ≥ 60 years (p = 0.03), race (<0.001), proximity to a livestock farm (0.01), proximity to farming raw materials services (0.02), and proximity to a nursing home (0.04) were independently associated with increased rates of CA-CDI.    Conclusions  Our study is the first to use spatial statistics and mixed models to identify important environmental risk factors for acquisition of  C .  difficile  and adds to the growing evidence that farm practices may put patients at risk for important drug-resistant infections.

plos one

Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses