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.

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.

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.

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.

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.

Globally to date, established international standards for animal welfare, a priority of sustainable agriculture, have primarily focused on large-scale producers. However, across Latin America, including in Ecuador’s Galápagos Islands, smallholder farms play a critical role in food safety and security. We assessed five basic animal welfare measures (feed and water access, shelter availability and housing systems, animal health management, animal behavior, and timely euthanasia) for poultry, pigs, and cattle on Floreana Island, Galápagos. Utilizing assessment standards from multiple US sources and international standards, we developed a questionnaire and used it to conduct in-depth interviews during 4–5 July 2022 with eight participating producers, representing 75% of animal agriculture on Floreana. While we identified opportunities to enhance competencies in animal health management and timely euthanasia, farms performed well in the other assessed measures. Future work should promote knowledge transfer and in-country capacity building in farm biosecurity, access to veterinary care, antimicrobial resistance surveillance, and euthanasia methods. Efforts to positively impact smallholder farm livelihoods in Galápagos—one of the most biodiverse and protected ecosystems on the planet—will sustainably support human health through the interconnected realms of animal health and welfare, wildlife and environmental health, and food safety and security.