Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
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
_version_ 1783236917680144384
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
work_keys_str_mv AT andersondeverickj identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT rojasleoncioflavio identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT watsonshera identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT knelsonlaurenp identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT pruittsohayla identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT lewissarahs identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT moehringrebekahw identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT sickbertbennettemilye identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT weberdavidj identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT chenlukef identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT sextondanielj identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses
AT identificationofnovelriskfactorsforcommunityacquiredclostridiumdifficileinfectionusingspatialstatisticsandgeographicinformationsystemanalyses