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Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses
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. diff...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433765/ https://www.ncbi.nlm.nih.gov/pubmed/28510584 http://dx.doi.org/10.1371/journal.pone.0176285 |
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author | Anderson, Deverick J. Rojas, Leoncio Flavio Watson, Shera Knelson, Lauren P. Pruitt, Sohayla Lewis, Sarah S. Moehring, Rebekah W. Sickbert Bennett, Emily E. Weber, David J. Chen, Luke F. Sexton, Daniel J. |
author_facet | Anderson, Deverick J. Rojas, Leoncio Flavio Watson, Shera Knelson, Lauren P. Pruitt, Sohayla Lewis, Sarah S. Moehring, Rebekah W. Sickbert Bennett, Emily E. Weber, David J. Chen, Luke F. Sexton, Daniel J. |
author_sort | Anderson, Deverick J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5433765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54337652017-05-26 Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses Anderson, Deverick J. Rojas, Leoncio Flavio Watson, Shera Knelson, Lauren P. Pruitt, Sohayla Lewis, Sarah S. Moehring, Rebekah W. Sickbert Bennett, Emily E. Weber, David J. Chen, Luke F. Sexton, Daniel J. PLoS One Research Article 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. Public Library of Science 2017-05-16 /pmc/articles/PMC5433765/ /pubmed/28510584 http://dx.doi.org/10.1371/journal.pone.0176285 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Anderson, Deverick J. Rojas, Leoncio Flavio Watson, Shera Knelson, Lauren P. Pruitt, Sohayla Lewis, Sarah S. Moehring, Rebekah W. Sickbert Bennett, Emily E. Weber, David J. Chen, Luke F. Sexton, Daniel J. Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title | Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title_full | Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title_fullStr | Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title_full_unstemmed | Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title_short | Identification of novel risk factors for community-acquired Clostridium difficile infection using spatial statistics and geographic information system analyses |
title_sort | identification of novel risk factors for community-acquired clostridium difficile infection using spatial statistics and geographic information system analyses |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433765/ https://www.ncbi.nlm.nih.gov/pubmed/28510584 http://dx.doi.org/10.1371/journal.pone.0176285 |
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