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

Anderson DJ1,2, Rojas LF2, Watson S2, Knelson LP2,3, Pruitt S4,5, Lewis SS1,2, Moehring RW1,2,6, Sickbert Bennett EE7, Weber DJ7, Chen LF1,2, Sexton DJ1,2; CDC Prevention Epicenters Program.

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Abstract

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.