Presence of Extended-Spectrum Beta-Lactamase-Producing Escherichia coli in Food-Producing and Companion Animals and Wildlife on Small-Holder Farms of Floreana Island, Galápagos Islands

Background: Antimicrobial resistance (AR) has led to increasing human and animal morbidity and mortality and negative consequences for the environment. AR among Escherichia coli (EC) is on the rise, with serious concerns about extended-spectrum β-lactamase-producing E. coli (ESBL-EC). In the Galápagos Islands, where antimicrobials are available without a prescription, growing demands for food production can drive antimicrobial use. Food producing animals are at the interface of wildlife and environmental health on the smallest human-inhabited Galápagos Island, Floreana. We sought to determine if ESBL-EC were present in Floreana Island farm animal species and nearby wildlife and the relatedness of ESBL-EC isolates identified. Materials and Methods: During July 4-5, 2022, we visited 8 multispecies farms, representing 75% of food-producing animal production on Floreana, and collected 227 fecal samples from farm animals and wildlife. Each sample was plated on MacConkey agar supplemented with cefotaxime (4 μg/mL). Results: ESBL-EC was isolated from 20 (9%) fecal samples collected from pigs (N = 10), chickens (N = 6), wildlife (N = 3), and dog (N = 1). All ESBL-EC isolates were from samples taken at three (38%) of the eight farms. Fifteen (75%) of the ESBL-EC isolates were from a single farm. All ESBL-EC isolates were multidrug resistant. The most prevalent ESBL genes belonged to the blaCTX-M group. Among the typeable isolates from the farm with the largest proportion of ESBL-EC isolates (N = 14), we observed nine unique pulsed-field gel electrophoresis (PFGE) patterns, with identical patterns present across pig and chicken isolates. PFGE patterns in the three farms with ESBL-EC isolates were different. Conclusions: These results lend support for future routine AR monitoring activities at the livestock-wildlife interface in Galápagos to characterize potential interspecies transmission of AR bacteria and AR genes in this unique protected ecosystem, and the related human, animal, and environmental health impacts, and to formulate interventions to reduce AR spread in this setting.

Assessing the impact of the early COVID-19 era on antibiotic-resistant threats in inpatient settings: A mixed Poisson regression approach

BACKGROUND

During the COVID-19 pandemic, increased antibiotic prescribing and infection prevention challenges coincided with antibiotic-resistant (AR) infection increases. Clostridioides difficile (C difficile) and methicillin-resistant Staphylococcus aureus (MRSA) are serious, costly AR threats. Health inequities in pandemic-era AR infections are not well-characterized.

METHODS

North Carolina statewide inpatient admissions were used to determine monthly admission rates and admission rate ratios (RRs) for C difficile and MRSA infections comparing 2017-2019 (prepandemic) to 2020 (pandemic exposure) using mixed-model Poisson regression adjusted for age, sex, comorbidities, and COVID-19. We assessed effect measure modification by admissions... community-level income, county rurality, and race and ethnicity. Mean total costs by infection type were compared.

RESULTS

With pandemic exposure, C difficile (adjusted RR.ß=.ß0.90 [95% confidence interval [CI] 0.86, 0.94]) and MRSA pneumonia (adjusted RR.ß=.ß0.97 [95% CI 0.91, 1.05]) decreased, while MRSA septicemia (adjusted RR.ß=.ß1.13 [95% CI 1.07, 1.19]) increased. Effect measure modification was not detected. C difficile or MRSA coinfection nearly doubled mean costs among COVID-19 admissions.

CONCLUSIONS

Despite decreases in C difficile and most MRSA infections, the early COVID-19 pandemic period saw continued increases in MRSA septicemia admissions in North Carolina. Equitable interventions to curb increases and reduce health care costs should be developed.

Geographic Variability, Seasonality, and Increase in ASPCA Animal Poison Control Center Harmful Blue-Green Algae Calls-United States and Canada, 2010-2022

Harmful cyanobacteria (blue-green algae) exposures can cause illness or death in humans and animals. We characterized American Society for the Prevention of Cruelty to Animals (ASPCA) Animal Poison Control Center (APCC) harmful blue-green algae (HBGA) call data, compared it to a measure of harmful algal bloom public awareness, and considered its suitability as a public health information source. ASPCA APCC dog and cat "HBGA exposure" calls made 1 January 2010-31 December 2022 were included. We calculated annual HBGA call percentages and described calls (species, month, origin, exposure route). We characterized public awareness by quantifying Nexis Uni® (LexisNexis Academic; New York, NY, USA)-indexed news publications (2010-2022) pertaining to "harmful algal bloom(s)". Call percentage increased annually, from 0.005% (2010) to 0.070% (2022). Of 999 HBGA calls, 99.4% (n = 993) were dog exposures. Over 65% (n = 655) of calls were made July-September, largely from the New England (n = 154 (15.4%)) and Pacific (n = 129 (12.9.%)) geographic divisions. Oral and dermal exposures predominated (n = 956 (95.7%)). Harmful algal bloom news publications increased overall, peaking in 2019 (n = 1834). Higher call volumes in summer and in the New England and Pacific geographic divisions drove HBGA call increases; public awareness might have contributed. Dogs and humans have similar exposure routes. ASPCA APCC HBGA call data could serve as a public health information source.

Enhancing the prediction of hospitalization from a COVID-19 agent-based model: A Bayesian method for model parameter estimation

Agent-based models (ABMs) have become a common tool for estimating demand for hospital beds during the COVID-19 pandemic. A key parameter in these ABMs is the probability of hospitalization for agents with COVID-19. Many published COVID-19 ABMs use either single point or age-specific estimates of the probability of hospitalization for agents with COVID-19, omitting key factors: comorbidities and testing status (i.e., received vs. did not receive COVID-19 test). These omissions can inhibit interpretability, particularly by stakeholders seeking to use an ABM for transparent decision-making. We introduce a straightforward yet novel application of Bayes' theorem with inputs from aggregated hospital data to better incorporate these factors in an ABM. We update input parameters for a North Carolina COVID-19 ABM using this approach, demonstrate sensitivity to input data selections, and highlight the enhanced interpretability and accuracy of the method and the predictions. We propose that even in tumultuous scenarios with limited information like the early months of the COVID-19 pandemic, straightforward approaches like this one with discrete, attainable inputs can improve ABMs to better support stakeholders.

Estimates of Presumed Population Immunity to SARS-CoV-2 by State in the United States, August 2021

Background

Information is needed to monitor progress toward a level of population immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sufficient to disrupt viral transmission. We estimated the percentage of the US population with presumed immunity to SARS-CoV-2 due to vaccination, natural infection, or both as of August 26, 2021.

Methods

Publicly available data as of August 26, 2021, from the Centers for Disease Control and Prevention were used to calculate presumed population immunity by state. Seroprevalence data were used to estimate the percentage of the population previously infected with SARS-CoV-2, with adjustments for underreporting. Vaccination coverage data for both fully and partially vaccinated persons were used to calculate presumed immunity from vaccination. Finally, we estimated the percentage of the total population in each state with presumed immunity to SARS-CoV-2, with a sensitivity analysis to account for waning immunity, and compared these estimates with a range of population immunity thresholds.

Results

In our main analysis, which was the most optimistic scenario, presumed population immunity varied among states (43.1% to 70.6%), with 19 states with ≤60% of their population having been infected or vaccinated. Four states had presumed immunity greater than thresholds estimated to be sufficient to disrupt transmission of less infectious variants (67%), and none were greater than the threshold estimated for more infectious variants (≥78%).

Conclusions

The United States remains a distance below the threshold sufficient to disrupt viral transmission, with some states remarkably low. As more infectious variants emerge, it is critical that vaccination efforts intensify across all states and ages for which the vaccines are approved.

Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity

The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19's impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC.

Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) Framework for Describing and Reporting Multidrug-resistant Organism and Healthcare-Associated Infections Agent-based Modeling Methods

Mathematical modeling of healthcare-associated infections and multidrug-resistant organisms improves our understanding of pathogen transmission dynamics and provides a framework for evaluating prevention strategies. One way of improving the communication among modelers is by providing a standardized way of describing and reporting models, thereby instilling confidence in the reproducibility and generalizability of such models. We updated the Overview, Design concepts, and Details protocol developed by Grimm et al [11] for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework includes the following 9 key elements: (1) Purpose and scope; (2) Entities, state variables, and scales; (3) Initialization; (4) Process overview and scheduling; (5) Input data; (6) Agent interactions and organism transmission; (7) Stochasticity; (8) Submodels; and (9) Model verification, calibration, and validation. Our objective is that this framework will improve the quality of evidence generated utilizing these models.

Contextual, Social and Epidemiological Characteristics of the Ebola Virus Disease Outbreak in Likati Health Zone, Democratic Republic of the Congo, 2017

While the clinical, laboratory and epidemiological investigation results of the Ebola outbreak in Likati Health Zone, Democratic Republic of the Congo (DRC) in May 2017 have been previously reported, we provide novel commentary on the contextual, social, and epidemiological characteristics of the epidemic. As first responders with the outbreak Surveillance Team, we explain the procedures that led to a successful epidemiological investigation and ultimately a rapid end to the epidemic. We discuss the role that several factors played in the trajectory of the epidemic, including traditional healers, insufficient knowledge of epidemiological case definitions, a lack of community-based surveillance systems and tools, and remote geography. We also demonstrate how a collaborative Rapid Response Team and implementation of community-based surveillance methods helped counter contextual challenges during the Likati epidemic and aid in identifying and reporting suspected cases and contacts in remote and rural settings. Understanding these factors can hinder or help in the rapid detection, notification, and response to future epidemics in the DRC.

Surveillance system assessment in Guinea: Training needed to strengthen data quality and analysis, 2016

The 2014–2016 Ebola virus disease outbreak revealed the fragility of the Guinean public health infrastructure. As a result, the Guinean Ministry of Health is collaborating with international partners to improve compliance with the International Health Regulations and work toward the Global Health Security Agenda goals, including enhanced case- and community-based disease surveillance. We assessed the case-based disease surveillance system during October 1, 2015–March 31, 2016, in the Boffa prefecture of Guinea. We conducted onsite interviews with public health staff at the peripheral (health center), middle (prefectural), and central (Ministry of Health) levels of the public health system to document leadership structure; methods for maintaining case registers and submitting weekly case reports; disease surveillance feedback; data analysis; and baseline surveillance information on four epidemic-prone diseases (cholera, meningococcal meningitis, measles, and yellow fever). The surveillance system was simple and paper-based at health centers and computer spreadsheet–based at the prefectural and central levels. Surveillance feedback to stakeholders at all levels was infrequent. Data analysis activities were minimal at the peripheral levels and progressively more robust at the prefectural and central levels. Reviewing the surveillance reports from Boffa during the study period, we observed zero reported cases of the four epidemic-prone diseases in the weekly reporting from the peripheral to the central level. Similarly, the national District Health Information System 2 had no reported cases of the four diseases in Boffa but did indicate reported cases among all four neighboring prefectures. Based on the assessment findings, which suggest low sensitivity of the case-based disease surveillance system in Boffa, we recommend additional training and support to improve surveillance data quality and enhance Guinean public health workforce capacity to use these data.

Modeling inpatient and outpatient antibiotic stewardship interventions to reduce the burden of Clostridioides difficile infection in a regional healthcare network

Antibiotic exposure can lead to unintended outcomes, including drug-drug interactions, adverse drug events, and healthcare-associated infections like Clostridioides difficile infection (CDI). Improving antibiotic use is critical to reduce an individual's CDI risk. Antibiotic stewardship initiatives can reduce inappropriate antibiotic prescribing (e.g., unnecessary antibiotic prescribing, inappropriate antibiotic selection), impacting both hospital (healthcare)-onset (HO)-CDI and community-associated (CA)-CDI. Previous computational and mathematical modeling studies have demonstrated a reduction in CDI incidence associated with antibiotic stewardship initiatives in hospital settings. Although the impact of antibiotic stewardship initiatives in long-term care facilities (LTCFs), including nursing homes, and in outpatient settings have been documented, the effects of specific interventions on CDI incidence are not well understood. We examined the relative effectiveness of antibiotic stewardship interventions on CDI incidence using a geospatially explicit agent-based model of a regional healthcare network in North Carolina. We simulated reductions in unnecessary antibiotic prescribing and inappropriate antibiotic selection with intervention scenarios at individual and network healthcare facilities, including short-term acute care hospitals (STACHs), nursing homes, and outpatient locations. Modeled antibiotic prescription rates were calculated using patient-level data on antibiotic length of therapy for the 10 modeled network STACHs. By simulating a 30% reduction in antibiotics prescribed across all inpatient and outpatient locations, we found the greatest reductions on network CDI incidence among tested scenarios, namely a 17% decrease in HO-CDI incidence and 7% decrease in CA-CDI. Among intervention scenarios of reducing inappropriate antibiotic selection, we found a greater impact on network CDI incidence when modeling this reduction in nursing homes alone compared to the same intervention in STACHs alone. These results support the potential importance of LTCF and outpatient antibiotic stewardship efforts on network CDI burden and add to the evidence that a coordinated approach to antibiotic stewardship across multiple facilities, including inpatient and outpatient settings, within a regional healthcare network could be an effective strategy to reduce network CDI burden.

Creation of a Geospatially Explicit, Agent-based Model of a Regional Healthcare Network with Application to Clostridioides difficile Infection

Agent-based models (ABMs) describe and simulate complex systems comprising unique agents, or individuals, while accounting for geospatial and temporal variability among dynamic processes. ABMs are increasingly used to study healthcare-associated infections (ie, infections acquired during admission to a healthcare facility), including Clostridioides difficile infection, currently the most common healthcare-associated infection in the United States. The overall burden and transmission dynamics of healthcare-associated infections, including C difficile infection, may be influenced by community sources and movement of people among healthcare facilities and communities. These complex dynamics warrant geospatially explicit ABMs that extend beyond single healthcare facilities to include entire systems (eg, hospitals, nursing homes and extended care facilities, the community). The agents in ABMs can be built on a synthetic population, a model-generated representation of the actual population with associated spatial (eg, home residence), temporal (eg, change in location over time), and nonspatial (eg, sociodemographic features) attributes. We describe our methods to create a geospatially explicit ABM of a major regional healthcare network using a synthetic population as microdata input. We illustrate agent movement in the healthcare network and the community, informed by patient-level medical records, aggregate hospital discharge data, healthcare facility licensing data, and published literature. We apply the ABM output to visualize agent movement in the healthcare network and the community served by the network. We provide an application example of the ABM to C difficile infection using a natural history submodel. We discuss the ABM's potential to detect network areas where disease risk is high; simulate and evaluate interventions to protect public health; adapt to other geographic locations and healthcare-associated infections, including emerging pathogens; and meaningfully translate results to public health practitioners, healthcare providers, and policymakers.

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) 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.

Assessing the Surveillance System for Priority Zoonotic Diseases in the Democratic Republic of the Congo, 2017

High-functioning communicable disease surveillance systems are critical for public health preparedness. Countries that cannot quickly detect and contain diseases are a risk to the global community. The ability of all countries to comply with the International Health Regulations is paramount for global health security. Zoonotic diseases can be particularly dangerous for humans. We conducted a surveillance system assessment of institutional and individual capacity in Kinshasa and Haut Katanga provinces in the Democratic Republic of the Congo for nationally identified priority zoonotic diseases (eg, viral hemorrhagic fever [VHF], yellow fever, rabies, monkeypox, and influenza monitored through acute respiratory infections). Data were collected from 79 health workers responsible for disease surveillance at 2 provincial health offices, 9 health zone offices, 9 general reference hospitals, and 18 health centers and communities. A set of questionnaires was used to assess health worker training in disease surveillance methods; knowledge of case definitions; availability of materials and tools to support timely case detection, reporting, and data interpretation; timeliness and completeness of reporting; and supervision from health authorities. We found that health workers either had not been recently or ever trained in surveillance methods and that their knowledge of case definitions was low. Timeliness and completeness of weekly notification of epidemic-prone diseases was generally well performed, but the lack of available standardized reporting forms and archive of completed forms affected the quality of data collected. Lessons learned from our assessment can be used for targeted strengthening efforts to improve global health security.

Hepatitis B Reverse Seroconversion and Transmission in a Hemodialysis Center: A Public Health Investigation and Case Report

In March 2013, public health authorities were notified of a new hepatitis B virus (HBV) infection in a patient receiving hemodialysis. We investigated to identify the source and prevent additional infections. We reviewed medical records, interviewed the index patient regarding hepatitis B risk factors, performed HBV molecular analysis, and observed infection control practices at the outpatient hemodialysis facility where she received care. The index patient's only identified hepatitis B risk factor was hemodialysis treatment. The facility had no other patients with known active HBV infection. One patient had evidence of a resolved HBV infection. Investigation of this individual, who was identified as the source patient, indicated that HBV reverse seroconversion and reactivation had occurred in the setting of HIV (human immunodeficiency virus) infection and a failed kidney transplant. HBV whole genome sequences analysis from the index and source patients indicated 99.9% genetic homology. Facility observations revealed multiple infection control breaches. Inadequate dilution of the source patient's sample during HBV testing might have led to a false-negative result, delaying initiation of hemodialysis in isolation. In conclusion, HBV transmission occurred after an HIV-positive hemodialysis patient with transplant-related immunosuppression experienced HBV reverse seroconversion and reactivation. Providers should be aware of this possibility, especially among severely immunosuppressed patients, and maintain stringent infection control.

Risk factors for hospitalization after dog bite injury: a case-cohort study of emergency department visits

OBJECTIVES

Dog bite injuries may result in pain, infection, emotional distress, dysfunction, and disfiguration, as well as lead to costly health care utilization, such as emergency department (ED) visits, rabies postexposure prophylaxis, and hospitalizations. Although clinical care guidelines exist, to our knowledge risk factors for hospitalization after a dog bite injury have not been examined quantitatively. Quantifying the magnitude of association between modifiable risk factors, such as infection, and hospitalization after a dog bite injury may guide intervention efforts, improve patient outcomes, and reduce unnecessary hospitalizations.

METHODS

A case-cohort study was conducted to examine the association between the following risk factors and hospitalization: infection, complicated injury, host-defense abnormality, number of previous evaluations for the injury, and anatomic location of the bite. The case-cohort design was chosen because cases could be identified in a well-defined administrative cohort, medical record review was required for each study patient, and the risk ratio was the effect measure of interest. The cohort consisted of ED patients evaluated for dog bite injuries between January 1, 2000 and December 31, 2011, at a large academic medical center. Cases were cohort members who were admitted as inpatients directly from the ED. From the cohort, a simple random sample was selected for the subcohort comparison group. Data on risk factors, the outcome, and covariates were collected from ED medical records. Logistic regression models, informed by directed acyclic graphs, were used to describe the relationship between each risk factor and hospitalization. Effect measure modification was examined by patient sex and race for the relationship between previous evaluation for the dog bite injury and hospitalization.

RESULTS

Cases (n = 111) were more likely to be male, white, or insured by Medicaid than were members of the subcohort (n = 221). The most common type of complicated injury was tendon or ligament injury for cases and fracture for the subcohort. All factors evaluated were associated with increased risk of hospitalization after dog bite injury. Yet, infection at the time of ED visit (odds ratio [OR] = 7.8, 95% confidence interval [CI] = 3.8 to 16.0) and injury to multiple anatomic locations (OR = 6.0, 95% CI = 1.2 to 30.9) were associated with the largest relative risks of hospitalization. For every three ED visits for infected dog bites, one resulted in hospitalization. Having had one or more prior evaluations for the dog bite injury was associated with a lower risk of hospitalization for females than males and for whites than nonwhites.

CONCLUSIONS

This study provides a unique, quantitative examination of risk factors for hospitalization after dog bite injury. The relative risk of hospitalization associated with each factor was substantial. The strongest association was for a modifiable risk factor, infection. This finding may inform best practices for initial care of patients with dog bite injuries and the development of novel protocols for following patients to reduce infections and subsequent hospitalizations.