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

Abstract 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. Disclosures All authors: No reported disclosures.


Background.
Positive externalities -beneficial spillover effects enjoyed by individuals who are not the primary consumers of a good -are rarely considered in cost-effectiveness analyses (CEAs) of antimicrobial drugs that could reduce person-to-person transmission of the target pathogen. We developed a compartmental model to simulate the effect of 2 hypothetical antibiotics targeting carbapenem-resistant Enterobacteriaceae (CRE) among hospital inpatients: one that treats bloodstream infections (BSIs) and one that decolonizes carriers. We assessed the contribution of positive externalities to the results of CEAs of these 2 antibiotics in the model. Methods. Our model tracked patients according to CRE carriage, clinical infection, and detection status. Rates of CRE acquisition depended on transmissibility of carriers in different states and were calibrated to data from long-term acute care hospitals. For the BSI treatment scenario we assumed the new drug would decrease the death rate and transmissibility of patients after CRE BSI onset. For the decolonization scenario we assumed the new drug would increase clearance of CRE carriage after clinical detection. For each scenario, we quantified the drug's effect on the number of BSIs and deaths among patients receiving the drug (direct effect) and among all patients (total effect, i.e., direct plus indirect effect) compared with usual care. For the CEAs, the effectiveness outcome was life-years (LYs) gained and we assumed the new drug cost of $4,000 per dose and cost of a CRE BSI of $24,788.
Results. For both the BSI treatment and decolonization scenarios, the total effect of introducing the new drug was greater than the direct effect alone, indicating the existence of positive externalities. Relative to usual care, the new drug led to a decrease in incremental cost and an increase in incremental effectiveness (see Figures 1 and 2).
Conclusion. The inclusion of positive externalities in CEAs can have important effects on whether these new antibiotics are deemed cost-effective, due to their potential for interrupting chains of transmission. In our model, the inclusion of these effects reduced the incremental cost and increased the incremental effectiveness of these antibiotics.

Disclosures. All authors:
No reported disclosures.

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.