Cargando…

2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence

BACKGROUND: Different antibiotic classes are associated with different Clostridioides difficile infection (CDI) risk. The impact of varied antibiotic risk on CDI incidence can be explored using agent-based models (ABMs). ABMs can simulate complete systems (e.g., regional healthcare networks) compris...

Descripción completa

Detalles Bibliográficos
Autores principales: Rhea, Sarah, Jones, Kasey, Bobashev, Georgiy, Munoz, Breda, Rineer, James, Hilscher, Rainer, DiBiase, Lauren, Sickbert-Bennett, Emily, Weber, David J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810361/
http://dx.doi.org/10.1093/ofid/ofz360.2118
_version_ 1783462233436585984
author Rhea, Sarah
Jones, Kasey
Bobashev, Georgiy
Munoz, Breda
Rineer, James
Hilscher, Rainer
DiBiase, Lauren
Sickbert-Bennett, Emily
Weber, David J
author_facet Rhea, Sarah
Jones, Kasey
Bobashev, Georgiy
Munoz, Breda
Rineer, James
Hilscher, Rainer
DiBiase, Lauren
Sickbert-Bennett, Emily
Weber, David J
author_sort Rhea, Sarah
collection PubMed
description BACKGROUND: Different antibiotic classes are associated with different Clostridioides difficile infection (CDI) risk. The impact of varied antibiotic risk on CDI incidence can be explored using agent-based models (ABMs). ABMs can simulate complete systems (e.g., regional healthcare networks) comprised of discrete, unique agents (e.g., patients) which can be represented using a synthetic population, or model-generated representation of the population. We used an ABM of a North Carolina (NC) regional healthcare network to assess the impact of increasing antibiotic risk ratios (RRs) across network locations on healthcare-associated (HA) and community-associated (CA) CDI incidence. METHODS: The ABM describes CDI acquisition and patient movement across 14 network locations (i.e., nodes) (11 short-term acute care hospitals, 1 long-term acute care hospital, 1 nursing home, and the community). We used a sample of 2 million synthetic NC residents as ABM microdata. We updated agent states (i.e., location, antibiotic exposure, C. difficile colonization, CDI status) daily. We applied antibiotic RRs of 1, 5, 8.9 (original model RR), 15, and 20 to agents across the network to simulate varied risk corresponding to different antibiotic classes. We determined network HA-CDI and CA-CDI incidence and percent mean change for each RR. RESULTS: In this simulation study, HA-CDI incidence increased with increasing antibiotic risk, ranging from 11.3 to 81.4 HA-CDI cases/100,000 person-years for antibiotic RRs of 1 to 20, respectively. On average, the per unit increase in antibiotic RR was 33% for HA-CDI and 6% for CA-CDI (figure). CONCLUSION: We used a geospatially explicit ABM to simulate increasing antibiotic risk, corresponding to different antibiotic classes, and to explore the impact on CDI incidence. The per unit increase in antibiotic risk was greater for HA-CDI than CA-CDI due to the higher probability of receiving antibiotics and higher concentration of agents with other CDI risk factors in the healthcare facilities of the ABM. These types of analyses, which demonstrate the interconnectedness of network healthcare facilities and the associated community served by the network, might help inform targeted antibiotic stewardship efforts in certain network locations. [Image: see text] DISCLOSURES: All authors: No reported disclosures.
format Online
Article
Text
id pubmed-6810361
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-68103612019-10-28 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence Rhea, Sarah Jones, Kasey Bobashev, Georgiy Munoz, Breda Rineer, James Hilscher, Rainer DiBiase, Lauren Sickbert-Bennett, Emily Weber, David J Open Forum Infect Dis Abstracts BACKGROUND: Different antibiotic classes are associated with different Clostridioides difficile infection (CDI) risk. The impact of varied antibiotic risk on CDI incidence can be explored using agent-based models (ABMs). ABMs can simulate complete systems (e.g., regional healthcare networks) comprised of discrete, unique agents (e.g., patients) which can be represented using a synthetic population, or model-generated representation of the population. We used an ABM of a North Carolina (NC) regional healthcare network to assess the impact of increasing antibiotic risk ratios (RRs) across network locations on healthcare-associated (HA) and community-associated (CA) CDI incidence. METHODS: The ABM describes CDI acquisition and patient movement across 14 network locations (i.e., nodes) (11 short-term acute care hospitals, 1 long-term acute care hospital, 1 nursing home, and the community). We used a sample of 2 million synthetic NC residents as ABM microdata. We updated agent states (i.e., location, antibiotic exposure, C. difficile colonization, CDI status) daily. We applied antibiotic RRs of 1, 5, 8.9 (original model RR), 15, and 20 to agents across the network to simulate varied risk corresponding to different antibiotic classes. We determined network HA-CDI and CA-CDI incidence and percent mean change for each RR. RESULTS: In this simulation study, HA-CDI incidence increased with increasing antibiotic risk, ranging from 11.3 to 81.4 HA-CDI cases/100,000 person-years for antibiotic RRs of 1 to 20, respectively. On average, the per unit increase in antibiotic RR was 33% for HA-CDI and 6% for CA-CDI (figure). CONCLUSION: We used a geospatially explicit ABM to simulate increasing antibiotic risk, corresponding to different antibiotic classes, and to explore the impact on CDI incidence. The per unit increase in antibiotic risk was greater for HA-CDI than CA-CDI due to the higher probability of receiving antibiotics and higher concentration of agents with other CDI risk factors in the healthcare facilities of the ABM. These types of analyses, which demonstrate the interconnectedness of network healthcare facilities and the associated community served by the network, might help inform targeted antibiotic stewardship efforts in certain network locations. [Image: see text] DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6810361/ http://dx.doi.org/10.1093/ofid/ofz360.2118 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Rhea, Sarah
Jones, Kasey
Bobashev, Georgiy
Munoz, Breda
Rineer, James
Hilscher, Rainer
DiBiase, Lauren
Sickbert-Bennett, Emily
Weber, David J
2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title_full 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title_fullStr 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title_full_unstemmed 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title_short 2440. Using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on Clostridioides difficile infection incidence
title_sort 2440. using a geospatially explicit agent-based model of a regional healthcare network to assess varied antibiotic risk on clostridioides difficile infection incidence
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810361/
http://dx.doi.org/10.1093/ofid/ofz360.2118
work_keys_str_mv AT rheasarah 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT joneskasey 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT bobashevgeorgiy 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT munozbreda 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT rineerjames 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT hilscherrainer 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT dibiaselauren 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT sickbertbennettemily 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence
AT weberdavidj 2440usingageospatiallyexplicitagentbasedmodelofaregionalhealthcarenetworktoassessvariedantibioticriskonclostridioidesdifficileinfectionincidence