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 ß-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.

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.

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.

Assessing Livestock Production Practices on Small-Scale Multi-Species Farms Located on Floreana Island, Galápagos Islands.

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.

Estimate of undetected severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in acute-care hospital settings using an individual-based microsimulation model.

OBJECTIVE: Current guidance states that asymptomatic screening for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) prior to admission to an acute-care setting is at the facility's discretion. This study's objective was to estimate the number of undetected cases of SARS-CoV-2 admitted as inpatients under 4 testing approaches and varying assumptions. DESIGN AND SETTING: Individual-based microsimulation of 104 North Carolina acute-care hospitals. PATIENTS: All simulated inpatient admissions to acute-care hospitals from December 15, 2021, to January 13, 2022 [ie, during the SARS-COV-2 ο (omicron) variant surge]. INTERVENTIONS: We simulated (1) only testing symptomatic patients, (2) 1-stage antigen testing with no confirmatory polymerase chain reaction (PCR) test, (3) 1-stage antigen testing with a confirmatory PCR for negative results, and (4) serial antigen screening (ie, repeat antigen test 2 days after a negative result). RESULTS: Over 1 month, there were 77,980 admissions: 13.7% for COVID-19, 4.3% with but not for COVID-19, and 82.0% for non-COVID-19 indications without current infection. Without asymptomatic screening, 1,089 (credible interval [CI], 946-1,253) total SARS-CoV-2 infections (7.72%) went undetected. With 1-stage antigen screening, 734 (CI, 638-845) asymptomatic infections (67.4%) were detected, with 1,277 false positives. With combined antigen and PCR screening, 1,007 (CI, 875-1,159) asymptomatic infections (92.5%) were detected, with 5,578 false positives. A serial antigen testing policy detected 973 (CI, 845-1,120) asymptomatic infections (89.4%), with 2,529 false positives. CONCLUSIONS: Serial antigen testing identified >85% of asymptomatic infections and resulted in fewer false positives with less cost per identified infection compared to combined antigen plus PCR testing.

Substance Use Disorder Treatment Providers' Knowledge and Opinions Toward Testing and Treatment of Chronic Hepatitis C in Rural North Carolina.

Poor access to care has made western North Carolina vulnerable to an outbreak of hepatitis C viral infection (HCV), particularly among persons who inject drugs (PWID). As substance use disorder (SUD) treatment providers could potentially improve linkage to HCV testing and treatment, we sought to understand SUD providers, clinic and client characteristics; referral patterns; HCV knowledge; willingness to participate in additional trainings; and local linkage-to-care pathways for treatment of substance use and HCV. Online survey data were collected from 78 SUD providers serving PWID in eight western rural North Carolina counties. Providers' attitudes toward working with HCV+ clients were very positive. One-third of providers reported a low fund of knowledge regarding HCV, HCV treatment, and financial assistance opportunities. Non-prescribing providers rarely initiated discussions about HCV testing/treatment, but were receptive to training. Respondents indicated that HCV testing and treatment were best delivered at local health departments or primary care clinics but were open to other venues where PWID access care. The vast majority of prescribing and non-prescribing providers expressed interest in obtaining training in HCV treatments, how to obtain HCV medications and topics on advanced liver disease. Data from prescribing and non-prescribing SUD providers suggest opportunities to develop or expand integrated care models for HCV testing/treatment in PWID in rural Appalachian North Carolina.

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.

Incorporation of near-real-time hospital occupancy data to improve hospitalization forecast accuracy during the COVID-19 pandemic.

Public health decision makers rely on hospitalization forecasts to inform COVID-19 pandemic planning and resource allocation. Hospitalization forecasts are most relevant when they are accurate, made available quickly, and updated frequently. We rapidly adapted an agent-based model (ABM) to provide weekly 30-day hospitalization forecasts (i.e., demand for intensive care unit [ICU] beds and non-ICU beds) by state and region in North Carolina for public health decision makers. The ABM was based on a synthetic population of North Carolina residents and included movement of agents (i.e., patients) among North Carolina hospitals, nursing homes, and the community. We assigned SARS-CoV-2 infection to agents using county-level compartmental models and determined agents' COVID-19 severity and probability of hospitalization using synthetic population characteristics (e.g., age, comorbidities). We generated weekly 30-day hospitalization forecasts during May-December 2020 and evaluated the impact of major model updates on statewide forecast accuracy under a SARS-CoV-2 effective reproduction number range of 1.0-1.2. Of the 21 forecasts included in the assessment, the average mean absolute percentage error (MAPE) was 7.8% for non-ICU beds and 23.6% for ICU beds. Among the major model updates, integration of near-real-time hospital occupancy data into the model had the largest impact on improving forecast accuracy, reducing the average MAPE for non-ICU beds from 6.6% to 3.9% and for ICU beds from 33.4% to 6.5%. Our results suggest that future pandemic hospitalization forecasting efforts should prioritize early inclusion of hospital occupancy data to maximize accuracy.

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.

Pharmacist attitudes and provision of harm reduction services in North Carolina: an exploratory study.

BACKGROUND: Pharmacists are among the most accessible healthcare providers in the United States and uniquely positioned to provide harm reduction services. The availability of pharmacy-based harm reduction services and pharmacist attitudes toward delivering these services have been understudied to date. We examine North Carolina (NC) pharmacists' experiences with and attitudes about harm reduction services and explore differences between rural and urban pharmacists. METHODS: A convenience sample of NC pharmacists participated in an anonymous, online survey regarding harm reduction services: non-prescription syringe sales; naloxone dispensing; and human immunodeficiency virus (HIV) and hepatitis C virus (HCV) screening. Urban-rural differences were analyzed using Pearson's chi-square or Fisher's exact tests. Open-ended responses were analyzed thematically. RESULTS: Three hundred pharmacists responded to the survey; 68 (23%) practiced in rural counties. Dispensing non-prescription syringes and naloxone at least occasionally was reported by 77% (n = 231) and 88% (n = 263) pharmacists, respectively. Pharmacy-delivered HIV or HCV screening was rare. Urban pharmacists dispensed naloxone more frequently than rural pharmacies (p = 0.04). Only 52% of pharmacists agreed that persons who inject drugs should always be allowed to buy non-prescription syringes. Rural pharmacists' attitudes toward harm reduction services for persons who inject drugs were statistically, though marginally, less supportive when compared to urban pharmacists' attitudes. The most common barrier to non-prescription syringe access was requiring patients to provide proof of prescription injection medication use, which 21% of pharmacists reported was required by their pharmacy's policy on non-prescription syringe sales. CONCLUSIONS: Although most pharmacies distributed naloxone and sold non-prescription syringes, pharmacy store policies and personal beliefs inhibited naloxone and non-prescription syringe dispensing. NC community pharmacies infrequently offer HIV and HCV screening. Paired with disseminating the evidence of the positive impact of harm reduction on individual and public health outcomes to NC pharmacists, institutional and systems changes to practice and policy may be important to promote harm reduction service availability, particularly for rural NC residents. TRIAL REGISTRATION: N/A.

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.

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.

Mitigating the Risk of Infectious Diseases Among Rural Drug Users in Western North Carolina: Results of the Southern Appalachia Test, Link, Care (SA-TLC) Health Care Provider Survey.

PURPOSE: To characterize how health care providers in western North Carolina (NC) manage patients with substance use disorders and to inform strategies for preventing injection drug use (IDU)-associated outbreaks of bloodborne infectious diseases. METHODS: We collected data on practice characteristics, provider sociodemographics, and attitudes and beliefs about hepatitis C virus (HCV), human immunodeficiency virus (HIV), opioid use, and IDU via online survey. Providers in 8 counties of western NC were invited to participate by email. Results were analyzed using descriptive and bivariate statistics. FINDINGS: Of 84 respondents participating between 30 July and 3 December 2018, 81% were practicing clinicians and 46% served a county identified as being vulnerable to IDU-associated outbreaks of HCV or HIV. A substantial proportion was unsure about injecting behaviors among patients. Scores reflected comfort working with opioid users, though this varied by medical specialty. One-quarter of respondents "never" discussed harm reduction or HCV treatment with patients known to inject drugs; 22% "never" discussed HIV screening with injectors; and 1 in 3 referred at-risk patients out for HCV or HIV testing rather than ordering a test themselves. Scores indicated low levels of stigma toward persons living with HCV or HIV. Respondents identified HIV treatment, HCV treatment, and liver disease management as training needs. CONCLUSIONS: Our findings provide insights to inform health infrastructure improvement, with the goal of preventing HCV or HIV outbreaks in southern Appalachia. Rural health care workers are willing to receive additional training if it can improve care for patients affected by substance use disorders.

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.

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.

Integrated Hepatitis C Testing and Linkage to Care at a Local Health Department Sexually Transmitted Disease Clinic: Determining Essential Resources and Evaluating Outcomes.

Guidance about integration of comprehensive hepatitis C virus (HCV)-related services in sexually transmitted disease (STD) clinics is limited. We evaluated a federally funded HCV testing and linkage-to-care program at an STD clinic in Durham County, North Carolina. During December 10, 2012, to March 31, 2015, the program tested 733 patients for HCV who reported 1 or more HCV risk factor; 81 (11%) were HCV-infected (ie, HCV antibody-positive and HCV ribonucleic acid-positive). Fifty-one infected patients (63%) were linked to care. We concluded that essential program resources include reflex HCV ribonucleic acid testing; a dedicated bridge counselor to provide test results, health education, and linkage-to-care assistance; and referral relationships for local HCV management and treatment.

HIV Preexposure Prophylaxis Implementation at Local Health Departments: A Statewide Assessment of Activities and Barriers.

BACKGROUND: Expanding access to HIV preexposure prophylaxis (PrEP) could help reduce rates of HIV infection in the United States. This study characterizes activities and barriers to PrEP implementation at local health departments (LHDs) in North Carolina (NC), which contains a large rural population. METHODS: In May 2016, a web-based survey was distributed to health directors of all county and district health departments in NC to assess PrEP-related activities, perceived barriers to PrEP implementation, and desired PrEP-related resources. RESULTS: Of 85 LHDs in NC, 56 (66%) responded to the survey. Of these, 2 (4%) reported PrEP prescribing and 7 (13%) externally referred for PrEP services. Among the 54 departments not prescribing PrEP, the most frequently cited reasons were cost concerns (n = 25, 46%), lack of formal prescribing protocols (n = 21, 39%), and belief that PrEP would be better managed at primary care or specialty clinics (n = 19, 35%). Among the 47 departments not prescribing or referring clients for PrEP, the most frequently cited reasons for the lack of PrEP referral were the absence of local PrEP providers (n = 29, 62%), lack of PrEP knowledge among staff (n = 13, 28%), and perceived lack of PrEP candidates (n = 12, 26%). The most frequently requested PrEP-related resources included training to help identify PrEP candidates (n = 39, 70%) and training on PrEP prescribing and management (n = 38, 68%). CONCLUSIONS: PrEP prescribing and referral among LHDs in NC remains extremely limited. Increased PrEP-related training and support for LHD-based providers could enhance PrEP access, especially in rural and underserved areas.