Model-based estimates of age-structured SARS-CoV-2 epidemiology in households.

Background: Understanding how infectious disease transmission varies from person to person, including associations with age and contact behavior, can help design effective control strategies. Within households, transmission may be highly variable because of differing transmission risks by age, household size, and individual contagiousness. Our aim was to disentangle those factors by fitting mathematical models to SARS-CoV-2 household survey and serologic data.

Validation and comparison of triage-based screening strategies for sepsis.

Objective: This study sought to externally validate and compare proposed methods for stratifying sepsis risk at emergency department (ED) triage.

Methods: This nested case/control study enrolled ED patients from four hospitals in Utah and evaluated the performance of previously-published sepsis risk scores amenable to use at ED triage based on their area under the precision-recall curve (AUPRC, which balances positive predictive value and sensitivity) and area under the receiver operator characteristic curve (AUROC, which balances sensitivity and specificity). Score performance for predicting whether patients met Sepsis-3 criteria in the ED was compared to patients' assigned ED triage score (Canadian Triage Acuity Score [CTAS]) with adjustment for multiple comparisons.

Predicting antibiotic resistance in Enterobacterales to support optimal empiric treatment of urinary tract infections in outpatient veterans.

Objective: Bacterial resistance is known to diminish the effectiveness of antibiotics for treatment of urinary tract infections. Review of recent healthcare and antibiotic exposures, as well as prior culture results is recommended to aid in selection of empirical treatment. However, the optimal approach for assessing these data is unclear.

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control.

During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making.

Trends in infection incidence and antimicrobial resistance in the US Veterans Affairs Healthcare System: a nationwide retrospective cohort study (2007-22).

Background: Antimicrobial resistance poses a major threat to public health. There are few comprehensive nationwide studies that quantify long-term trends in infection incidence and antimicrobial resistance for multiple pathogens. We aimed to analyse trends in inpatient infection incidence and antimicrobial resistance for nine pathogens over the past 15 years across the USA.

Importance of underlying mechanisms for interpreting relative risk of Clostridioides difficile infection among antibiotic-exposed patients in healthcare facilities.

Clostridioides difficile infection (CDI) is a significant public health threat, associated with antibiotic-induced disruption of the normally protective gastrointestinal microbiota. CDI is thought to occur in two stages: acquisition of asymptomatic colonization from ingesting C. difficile bacteria followed by progression to symptomatic CDI caused by toxins produced during C.

Infectious disease surveillance needs for the United States: lessons from Covid-19.

The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S.

Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans.

Objective: To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention.

Data Sources And Study Setting: Interviews conducted from March-May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status.

Study Design: Interview questions were developed using Petersen et al.

Effectiveness and Safety of an Emergency Department Code Sepsis Protocol: A Pragmatic Clinical Trial.

Sepsis care delivery-including the initiation of prompt, appropriate antimicrobials-remains suboptimal. This study was conducted to determine direct and off-target effects of emergency department (ED) sepsis care reorganization. This pragmatic pilot trial enrolled adult patients who presented from November 2019 to February 2021 to an ED in Utah before and after implementation of a multimodal, team-based "Code Sepsis" protocol.

Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review.

Unlabelled: During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US.

Factors associated with testing positive for SARS-CoV-2 and evaluation of a recruitment protocol among healthcare personnel in a COVID-19 vaccine effectiveness study.

Objective: The objective of this study was to determine factors associated with testing positive for SARS-CoV-2 among healthcare personnel. Secondary objectives were to assess representativeness of recruited participants and the effectiveness of a multiple-contact protocol for recruiting healthcare personnel in this COVID-19 study.

Design: Survey study, conducted as part of an observational test-negative study of COVID-19 vaccine effectiveness.

A Linguistic Analysis Examining the Impact of COVID-19 on Pneumonia Diagnosis and Disease Models.

Written clinical language embodies and reflects the clinician's mental models of disease. Prior to the COVID-19 pandemic, pneumonia was shifting away from concern for healthcare-associated pneumonia and toward recognition of heterogeneity of pathogens and host response. How these models are reflected in clinical language or whether they were impacted by the pandemic has not been studied.

An economic evaluation of the expansion of electronic case reporting in an academic healthcare setting.

Objectives: Determine the economic cost or benefit of expanding electronic case reporting (eCR) for 29 reportable conditions beyond the initial eCR implementation for COVID-19 at an academic health center.

Materials And Methods: The return on investment (ROI) framework was used to quantify the economic impact of the expansion of eCR from the perspective of an academic health system over a 5-year time horizon. Sensitivity analyses were performed to assess key factors such as personnel cost, inflation, and number of expanded conditions.

Navigating the Landscape: Barriers and Facilitators in Electronic Case Reporting Implementation Across Public Health Agencies.

Objective: The objectives were to identify barriers and facilitators for electronic case reporting (eCR) implementation associated with "organizational" and "people"-based knowledge/processes and to identify patterns across implementation stages to guide best practices for eCR implementation at public health agencies.

Design: This qualitative study uses semistructured interviews with key stakeholders across 6 public health agencies. This study leveraged 2 conceptual frameworks for the development of the interview guide and initial codebook and the organization of the findings of thematic analysis.

Scoping review of modelling studies assessing the impact of disruptions to essential health services during COVID-19.

Background: Studies assessing the indirect impact of COVID-19 using mathematical models have increased in recent years. This scoping review aims to identify modelling studies assessing the potential impact of disruptions to essential health services caused by COVID-19 and to summarise the characteristics of disruption and the models used to assess the disruptions.

Methods: Eligible studies were included if they used any models to assess the impact of COVID-19 disruptions on any health services.

Disparities in COVID-19 related outcomes in the United States by race and ethnicity pre-vaccination era: an umbrella review of meta-analyses.

Background: Meta-analyses have investigated associations between race and ethnicity and COVID-19 outcomes. However, there is uncertainty about these associations' existence, magnitude, and level of evidence. We, therefore, aimed to synthesize, quantify, and grade the strength of evidence of race and ethnicity and COVID-19 outcomes in the US.

Differences in perceptions and acceptance of COVID-19 vaccination between vaccine hesitant and non-hesitant persons.

Acceptance of the COVID-19 vaccination becomes more critical as new variants continue to evolve and the United States (US) attempts to move from pandemic response to management and control. COVID-19 stands out in the unique way it has polarized patients and generated sustained vaccine hesitancy over time. We sought to understand differences in perceptions and acceptance of COVID-19 vaccination between vaccine hesitant and non-hesitant patients, with the goal of informing communication and implementation strategies to increase uptake of COVID-19 vaccines in Veteran and non-Veteran communities.

Choosing Veterans Affairs: Determinants of post-9/11 Veterans' enrollment in Veterans Affairs health care.

Following recent policy changes, younger Veterans have particularly increased options for where to receive their health care. Although existing research provides some understanding of non-modifiable individual (e.g.

Sex difference in the discordance between Abbott Architect and EuroImmun serological assays.

Background: This study evaluated the discordance between Abbott Architect SARS-CoV-2 IgG and EUROIMMUN SARS-COV-2 ELISA in a seroprevalence study.

Methods: From June 10 to August 15, 2020, 8,246 specimens were dually evaluated by the Abbott Architect SARS-CoV-2 IgG (Abbott) and the EUROIMMUN SARS-CoV-2 ELISA (EI) assays. Sex-stratified phi correlation coefficients were calculated to evaluate the concordance between Abbott and EI assay's quantitative results.

Implementation of an Antibiotic Stewardship Initiative in a Large Urgent Care Network.

Importance: Urgent Care (UC) encounters result in more inappropriate antibiotic prescriptions than other outpatient setting. Few stewardship interventions have focused on UC.

Objective: To evaluate the effectiveness of an antibiotic stewardship initiative to reduce antibiotic prescribing for respiratory conditions in a UC network.

Assessing Pathogen Transmission Opportunities: Variation in Nursing Home Staff-Resident Interactions.

Objectives: The Centers for Disease Control and Prevention (CDC) recommends implementing Enhanced Barrier Precautions (EBP) for all nursing home (NH) residents known to be colonized with targeted multidrug-resistant organisms (MDROs), wounds, or medical devices. Differences in health care personnel (HCP) and resident interactions between units may affect risk of acquiring and transmitting MDROs, affecting EBP implementation. We studied HCP-resident interactions across a variety of NHs to characterize MDRO transmission opportunities.

A qualitative evaluation of frontline clinician perspectives toward antibiotic stewardship programs.

Objective: To examine the perspectives of caregivers that are not part of the antibiotic stewardship program (ASP) leadership team (eg, physicians, nurses, and clinical pharmacists), but who interact with ASPs in their role as frontline healthcare workers.

Design: Qualitative semistructured interviews.

Setting: The study was conducted in 2 large national healthcare systems including 7 hospitals in the Veterans' Health Administration and 4 hospitals in Intermountain Healthcare.

Electronic surveillance criteria for non-ventilator-associated hospital-acquired pneumonia: Assessment of reliability and validity.

Objective: Surveillance of non-ventilator-associated hospital-acquired pneumonia (NV-HAP) is complicated by subjectivity and variability in diagnosing pneumonia. We compared a fully automatable surveillance definition using routine electronic health record data to manual determinations of NV-HAP according to surveillance criteria and clinical diagnoses.

Methods: We retrospectively applied an electronic surveillance definition for NV-HAP to all adults admitted to Veterans' Affairs (VA) hospitals from January 1, 2015, to November 30, 2020.

Hospital admission decisions for older Veterans with community-onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers.

Objectives: Age is important for prognosis in community-onset pneumonia, but how it influences admission decisions in the emergency department (ED) is not well characterized. Using clinical data from the electronic health record in a national cohort, we examined pneumonia hospitalization patterns, variation, and relationships with mortality among older versus younger Veterans.

Methods: In a retrospective cohort of patients ≥ 18 years presenting to EDs with a diagnosis of pneumonia at 118 VA Medical Centers January 1, 2006, to December 31, 2016, we compared observed, predicted, and residual hospitalization risk for Veterans < 70, 70-79, and ≥ 80 years of age using generalized estimating equations and machine learning models with 71 patient factors.