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...
Autores principales: | , , , , , , , , |
---|---|
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 |