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. 2017 May 16;12(5):e0176285.
doi: 10.1371/journal.pone.0176285. eCollection 2017.

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

Deverick J Anderson  1   2 , Leoncio Flavio Rojas  2 , Shera Watson  2 , Lauren P Knelson  2   3 , Sohayla Pruitt  4   5 , Sarah S Lewis  1   2 , Rebekah W Moehring  1   2   6 , Emily E Sickbert Bennett  7 , David J Weber  7 , Luke F Chen  1   2 , Daniel J Sexton  1   2 ; CDC Prevention Epicenters Program
Affiliations

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

Deverick J Anderson et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: Sohayla Pruitt was employed by Duke Health Technology Solutions during the course of the study and moved to Forecast Health after completion of the study. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Case location of 1,895 cases of community-associated Clostridium difficile infection in the 10-county study area in central North Carolina.
*Census tract size is inversely proportional to population density. Grey dots represent individual cases. North is oriented to the top of the page. MAP SOURCE: Map created using ArcGIS software by Esri using TeleAtlas and US Census data sources.
Fig 2
Fig 2. “Hot spots” or clusters of community-acquired CDI in a 10-county area in central North Carolina.
North is oriented to the top of the page. MAP SOURCE: Map created using ArcGIS software by Esri using TeleAtlas and US Census data sources.
See this image and copyright information in PMC

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