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. METHODS: We surveyed members of 3,381 Utah households from January-April 2021 and performed SARS-CoV-2 antibody testing on all available members. We paired these data with a probabilistic model of household importation and transmission composed of a novel combination of transmission variability and age- and size-structured heterogeneity. We calculated maximum likelihood estimates of mean and variability of household transmission probability between household members in different age groups and different household sizes, simultaneously with importation probability and probabilities of false negative and false positive test results. RESULTS: 12.8% of individual participants, residing in 17.4% of the participating households, showed serologic evidence of prior infection or reported a prior positive test on the survey. Serologically positive individuals in younger age groups were less likely than older adults to have tested positive during their infection according to our survey results. Our model results suggested that adolescents and young adults (ages 13-24) acquired SARS-CoV-2 infection outside the household at a rate substantially higher than younger children and older adults. Our estimate of the household secondary attack rate (HSAR) among adults aged 45 and older exceeded HSARs to and/or from younger age groups at a given household size. We found lower HSAR in households with more members, independent of age differences. The age-specific HSAR patterns we found could not be explained by age-dependent biological susceptibility and transmissibility alone, suggesting that age groups contacted each other at different rates within households. CONCLUSIONS: We disentangled several factors contributing to age-specific infection risk, including non-household exposure, within-household exposure to specific age groups, and household size. Within-household contact rate differences played a significant role in driving household transmission epidemiology. These findings provide nuanced insights for understanding community outbreak patterns and mechanisms of differential infection risk.

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. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

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. We utilized data from the Veterans Health Administration to evaluate relationships between culture and treatment history and the subsequent probability of antibiotic-resistant bacteria identified in urine cultures to further guide clinicians in understanding these risk factors. Methods: Using the XGBoost algorithm, a retrospective cohort of outpatients with urine culture results and antibiotic prescriptions from 2017 to 2022 was used to develop models for predicting antibiotic resistance for three classes of antibiotics: cephalosporins, fluoroquinolones, and trimethoprim/sulfamethoxazole (TMP/SMX) obtained from urine cultures. Model performance was assessed using Area Under the Receiver Operating Characteristic curve (AUC) and Precision-Recall AUC (PRAUC). Results: There were 392,647 prior urine cultures identified in 214,656 patients. A history of bacterial resistance to the specific treatment was the most important predictor of subsequent resistance for positive cultures, followed by a history of specific antibiotic exposure. The models performed better than previously established risk factors alone, especially for fluoroquinolone resistance, with an AUC of .84 and PRAUC of .70. Notably, the models' performance improved markedly (AUC = .90, PRAUC = .87) when applied to cultures from patients with a known history of resistance to any of the antibiotic classes. Conclusion: These predictive models demonstrate potential in guiding antibiotic prescription and improving infection management.

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. RESULTS: Among 2000 case/control patients, 981 met Sepsis-3 criteria on final adjudication. The best performing sepsis risk scores were the Predict Sepsis version #3 (AUPRC 0.183, 95 % CI 0.148-0.256; AUROC 0.859, 95 % CI 0.843-0.875) and Borelli scores (AUPRC 0.127, 95 % CI 0.107-0.160, AUROC 0.845, 95 % CI 0.829-0.862), which significantly outperformed CTAS (AUPRC 0.038, 95 % CI 0.035-0.042, AUROC 0.650, 95 % CI 0.628-0.671, p < 0.001 for all AUPRC and AUROC comparisons). The Predict Sepsis and Borelli scores exhibited sensitivity of 0.670 and 0.678 and specificity of 0.902 and 0.834, respectively, at their recommended cutoff values and outperformed Systemic Inflammatory Response Syndrome (SIRS) criteria (AUPRC 0.083, 95 % CI 0.070-0.102, p = 0.052 and p = 0.078, respectively; AUROC 0.775, 95 % CI 0.756-0.795, p < 0.001 for both scores). CONCLUSIONS: The Predict Sepsis and Borelli scores exhibited improved performance including increased specificity and positive predictive values for sepsis identification at ED triage compared to CTAS and SIRS criteria.

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. METHODS: In this US nationwide retrospective cohort study, we analysed clinical microbiology data from electronic health records from all patients admitted to all 138 Veterans Affairs (VA) Medical Centers with acute care wards across the USA from Feb 1, 2007, to March 31, 2022. We quantified inpatient antibiotic use as days of therapy (DOT) per 1000 patient-days and antimicrobial resistance by resistance proportion (proportion of incident isolates identified as resistant) and phenotypic incidence (incidence of infections per 1000 admissions classified as resistant, susceptible, or missing). To analyse trends before the COVID-19 pandemic and during the COVID-19 pandemic, we used generalised estimating equation models and reported average annual percentage changes (AAPC). FINDINGS: We collected 991 527 30-day incident isolates from 507 760 patients in 138 VA Medical Centers and 50 states in the USA. Between Feb 1, 2007, and Dec 31, 2019, infection incidence and antimicrobial resistance declined for many pathogens and pathogen-drug combinations. The proportion of methicillin resistance in Staphylococcus aureus decreased from 57·7% (11 876 of 20 584 incident isolates) to 44·6% (5916 of 13 257) over these 13 years (AAPC -1·8%; 95% CI -2·4 to -1·2; p<0·0001), and vancomycin-resistant Enterococcus faecium infections decreased from 77·8% (2555 of 3285) to 65·1% (893 of 1371; AAPC -1·2%; 95% CI -2·5 to 0·0; p=0·052). Fluoroquinolone resistance declined in both proportion and incidence for most pathogens. These trends correlated with substantial reductions in fluoroquinolone use, from 125 DOT per 1000 patient-days to 20 DOT per 1000 patient-days. Third generation cephalosporin resistance increased steeply in Escherichia coli infections from 6·7% (942 of 14 042) in 2007 to 15·3% (2153 of 14 053) in 2019 (AAPC 8·5%; 95% CI 6·2 to 10·7; p<0·0001). Carbapenem resistance proportion increased in Enterobacter cloacae infections from 1·1% (30 of 2852) in 2007 to 7·3% (212 of 2919) in 2019 (AAPC 19·8%; 95% CI 13·7 to 26·2; p<0·0001), but remained low for Klebsiella pneumoniae and E coli. During the COVID-19 pandemic between Jan 1, 2020, and March 31, 2022, several pathogen-drug combinations increased in both incidence and resistance for hospital-associated infections. For some pathogen-drug combinations, trends in incidence of resistant and susceptible infections were divergent, whereas for other combinations, these trends were in the same direction. INTERPRETATION: Significant reductions in methicillin resistance in S aureus, vancomycin-resistant E faecium, and fluoroquinolone resistance across multiple pathogens suggest that control efforts have had an effect on resistance. The rise in extended-spectrum ß-lactamases-producing Enterobacterales and recent surge in hospital-associated infections emphasise the need for ongoing surveillance and interventions. Our study highlights how coupling the analysis of phenotypic incidence with resistance proportion can enhance interpretation of antimicrobial resistance data. FUNDING: US Centers for Disease Control and Prevention.

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. difficile overgrowth. The degree to which disruptive antibiotic exposure increases susceptibility at each stage is uncertain, which might contribute to divergent published projections of the impact of hospital antibiotic stewardship interventions on CDI. Here, we model C. difficile transmission and CDI among hospital inpatients, including exposure to high-CDI-risk antibiotics and their effects on each stage of CDI epidemiology. We derive the mathematical relationship, using a deterministic model, between those parameters and observed equilibrium levels of colonization, CDI, and risk ratio of CDI among certain antibiotic-exposed patients relative to patients with no recent antibiotic exposure. We then quantify the sensitivity of projected antibiotic stewardship intervention impacts to alternate assumptions. We find that two key parameters, the antibiotic effects on susceptibility to colonization and to CDI progression, are not identifiable given the data frequently available. Furthermore, the effects of antibiotic stewardship interventions are sensitive to their assumed values. Thus, discrepancies between different projections of antibiotic stewardship interventions may be largely due to model assumptions. Data supporting improved quantification of mechanistic antibiotic effects on CDI epidemiology are needed to understand stewardship effects better.

Diagnostic Discordance, Uncertainty, and Treatment Ambiguity in Community-Acquired Pneumonia : A National Cohort Study of 115 U.S. Veterans Affairs Hospitals.

BACKGROUND: Evidence-based practice in community-acquired pneumonia often assumes an accurate initial diagnosis. OBJECTIVE: To examine the evolution of pneumonia diagnoses among patients hospitalized from the emergency department (ED). DESIGN: Retrospective nationwide cohort. SETTING: 118 U.S. Veterans Affairs medical centers. PATIENTS: Aged 18 years or older and hospitalized from the ED between 1 January 2015 and 31 January 2022. MEASUREMENTS: Discordances between initial pneumonia diagnosis, discharge diagnosis, and radiographic diagnosis identified by natural language processing of clinician text, diagnostic coding, and antimicrobial treatment. Expressions of uncertainty in clinical notes, patient illness severity, treatments, and outcomes were compared. RESULTS: Among 2 383 899 hospitalizations, 13.3% received an initial or discharge diagnosis and treatment of pneumonia: 9.1% received an initial diagnosis and 10.0% received a discharge diagnosis. Discordances between initial and discharge occurred in 57%. Among patients discharged with a pneumonia diagnosis and positive initial chest image, 33% lacked an initial diagnosis. Among patients diagnosed initially, 36% lacked a discharge diagnosis and 21% lacked positive initial chest imaging. Uncertainty was frequently expressed in clinical notes (58% in ED; 48% at discharge); 27% received diuretics, 36% received corticosteroids, and 10% received antibiotics, corticosteroids, and diuretics within 24 hours. Patients with discordant diagnoses had greater uncertainty and received more additional treatments, but only patients lacking an initial pneumonia diagnosis had higher 30-day mortality than concordant patients (14.4% [95% CI, 14.1% to 14.7%] vs. 10.6% [CI, 10.4% to 10.7%]). Patients with diagnostic discordance were more likely to present to high-complexity facilities with high ED patient load and inpatient census. LIMITATION: Retrospective analysis; did not examine causal relationships. CONCLUSION: More than half of all patients hospitalized and treated for pneumonia had discordant diagnoses from initial presentation to discharge. Treatments for other diagnoses and expressions of uncertainty were common. These findings highlight the need to recognize diagnostic uncertainty and treatment ambiguity in research and practice of pneumonia-related care. PRIMARY FUNDING SOURCE: The Gordon and Betty Moore Foundation.

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.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process. DATA COLLECTION/EXTRACTION METHODS: Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews. PRINCIPAL FINDINGS: Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems. CONCLUSIONS: Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.

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

RATIONALE: Sepsis care delivery - including initiation of prompt, appropriate antimicrobials - remains suboptimal. OBJECTIVE: Determine direct and off-target effects of emergency department (ED) sepsis care reorganization. METHODS: This pragmatic pilot trial enrolled adult patients presenting November 2019 to February 2021 to an ED in Utah before and after implementation of a multimodal, team-based "Code Sepsis" protocol. Patients presenting to two other EDs where usual care was continued served as contemporaneous controls. The primary outcome was door-to-antimicrobial time among patients meeting Sepsis-3 criteria before ED departure. Secondary and safety outcomes included all-cause 30-day mortality, antimicrobial utilization and overtreatment, and antimicrobial-associated adverse events. Multivariable regression analyses employed difference-in-differences methods to account for trends in outcomes unrelated to the studied intervention. RESULTS: Code Sepsis protocol activation (N=307) exhibited 8.5% sensitivity and 66% positive predictive value for patients meeting sepsis criteria before ED departure. Among 10,151 patients meeting sepsis criteria during the study, adjusted difference-in-differences analysis demonstrated a 13-minute (95% CI 7-19-minute) decrease in door-to-antimicrobial time associated with Code Sepsis implementation (p<0.001). Mortality and clinical safety outcomes were unchanged, but Code Sepsis implementation was associated with increased false-positive presumptive infection diagnosis among patients meeting sepsis criteria in the ED and increased antimicrobial utilization. CONCLUSIONS: Implementation of a team-based protocol for rapid sepsis evaluation and treatment during the COVID-19 pandemic's first year was associated with decreased ED door-to-antimicrobial time but also increased antimicrobial utilization. Measurement of both patient-centered and off-target effects of sepsis care improvement interventions is essential to comprehensive assessment of their value. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT04148989) This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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

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. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding: This research was funded by the Centers for Disease Control and Prevention (CDC).

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., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

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. Setting: University of Utah Health system, including both inpatient and outpatient facilities. Participants: Clinical and non-clinical healthcare personnel at University of Utah Health. 1456 were contacted and 503 (34.5%) completed the survey. Cases were all eligible employees testing positive for COVID-19, with 3:1 randomly selected, matched controls (test negative) selected weekly. Methods: Online survey. Results: Significant differences in the demographics of participants and the source population were observed; e.g., nursing staff comprised 31.6% of participants but only 23.3% of the source population. The multiple-contact recruitment protocol increased participation by ten percentage points and ensured equal representation of controls. Potential exposure to illness outside of work was strongly predictive of testing positive for SARS-CoV-2 (OR = 3.74; 95% CI: 2.29, 6.11) whereas potential exposure at work was protective against testing positive (OR: 0.51, 95% CI: 0.29, 0.88). Conclusions: Carefully designed recruitment protocols increase participation and representation of controls, but bias in participant demographics still exists. The negative association between potential workplace exposure and positive test suggests testing bias in the test-negative design. Healthcare personnel's potential exposures to COVID-19 outside of the workplace are important predictors of SARS-CoV-2 seropositivity.

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. We aimed to assess changes in the language used to describe pneumonia following the COVID-19 pandemic.

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. Results: The total implementation costs for the implementation year were estimated to be $5031.46. The 5-year ROI for the expansion of eCR for the 29 conditions is expected to be 142% (net present value of savings: $7166). Based on the annual ROI, estimates suggest that the savings from the expansion of eCR will cover implementation costs in approximately 4.8 years. All sensitivity analyses yielded a strong ROI for the expansion of eCR. Discussion and conclusion: Our findings suggest a strong ROI for the expansion of eCR at UHealth, with the most significant cost savings observed implementing eCR for all reportable conditions. An early effort to ensure data quality is recommended to expedite the transition from parallel reporting to production to improve the ROI for healthcare organizations. This study demonstrates a positive ROI for the expansion of eCR to additional reportable conditions beyond COVID-19 in an academic health setting, such as UHealth. While this evaluation focuses on the 5-year time horizon, the potential benefit could extend further.

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. SETTING: Interviews were conducted virtually with informants from public health agencies at varying stages of eCR implementation. PARTICIPANTS: Investigators aimed to enroll 3 participants from each participating public health agency, including an eCR lead, a technical lead, and a leadership informant. MAIN OUTCOME MEASURES: Patterns associated with barriers and facilitators across the eCR implementation stage. RESULTS: Twenty-eight themes were identified throughout interviews with 16 informants representing 6 public health agencies at varying stages of implementation. While there was variation across these levels, 3 distinct patterns were identified, including themes that were described (1) solely as a barrier or facilitator for eCR implementation regardless of implementation stages, (2) as a barrier for those in the early stages but evolved into a facilitator for those in later stages, and (3) as facilitators that were unique to the late-stage implementation. CONCLUSION: This study elucidated critical national, organizational, and person-centric best practices for public health agencies. These included the importance of engagement with the national eCR team, integrated development teams, cross-pollination, and developing solutions with the broader public health mission in mind. While the implementation of eCR was the focus of this study, the findings are generalizable to the broader data modernization efforts within public health agencies.

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. Methods: In this umbrella review, we searched four databases (Pubmed, Embase, the Cochrane Database of Systematic Reviews, and Epistemonikos) from database inception to April 2022. The methodological quality of each meta-analysis was assessed using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR-2). The strength of evidence of the associations between race and ethnicity with outcomes was ranked according to established criteria as convincing, highly suggestive, suggestive, weak, or non-significant. The study protocol was registered with PROSPERO, CRD42022336805. Results: Of 880 records screened, we selected seven meta-analyses for evidence synthesis, with 42 associations examined. Overall, 10 of 42 associations were statistically significant (p ≤ 0.05). Two associations were highly suggestive, two were suggestive, and two were weak, whereas the remaining 32 associations were non-significant. The risk of COVID-19 infection was higher in Black individuals compared to White individuals (risk ratio, 2.08, 95% Confidence Interval (CI), 1.60-2.71), which was supported by highly suggestive evidence; with the conservative estimates from the sensitivity analyses, this association remained suggestive. Among those infected with COVID-19, Hispanic individuals had a higher risk of COVID-19 hospitalization than non-Hispanic White individuals (odds ratio, 2.08, 95% CI, 1.60-2.70) with highly suggestive evidence which remained after sensitivity analyses. Conclusion: Individuals of Black and Hispanic groups had a higher risk of COVID-19 infection and hospitalization compared to their White counterparts. These associations of race and ethnicity and COVID-19 outcomes existed more obviously in the pre-hospitalization stage. More consideration should be given in this stage for addressing health inequity.

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. This qualitative study used interview data from focus groups conducted by the Department of Veterans Affairs (VA) and the University of Utah; all focus groups were conducted using the same script March-July 2021. Groups included forty-six United States Veterans receiving care at 28 VA facilities across the country and 166 non-Veterans across Utah for a total of 36 one-hour focus groups. We identified perceptions and attitudes toward COVID-19 vaccination through qualitative analysis of focus group participant remarks, grouping connections with identified themes within domains developed based on the questions asked in the focus group guide. Responses suggest participant attitudes toward the COVID-19 vaccine were shaped primarily by vaccine attitude changes over time, impacted by perceived vaccine benefits, risks, differing sources of vaccine information and political ideology. Veterans appeared more polarized, being either largely non-hesitant, or hesitant, whereas non-Veterans had a wider range of hesitancy, with more participants identifying minor doubts and concerns about receiving the vaccine, or simply being altogether unsure about receiving it. Development of COVID-19 vaccine communication strategies in Veteran and non-Veteran communities should anticipate incongruous sources of information and explicitly target community differences in perceptions of risks and benefits associated with the vaccine to generate candid discussions and repair individuals' trust. We believe this could accelerate vaccine acceptance over time.

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. Articles published from January 2020 to December 2022 were identified from PubMed, Embase and CINAHL, using detailed searches with key concepts including COVID-19, modelling and healthcare disruptions. Two reviewers independently extracted the data in four domains. A descriptive analysis of the included studies was performed under the format of a narrative report. RESULTS: This scoping review has identified a total of 52 modelling studies that employed several models (n=116) to assess the potential impact of disruptions to essential health services. The majority of the models were simulation models (n=86; 74.1%). Studies covered a wide range of health conditions from infectious diseases to non-communicable diseases. COVID-19 has been reported to disrupt supply of health services, demand for health services and social change affecting factors that influence health. The most common outcomes reported in the studies were clinical outcomes such as mortality and morbidity. Twenty-five studies modelled various mitigation strategies; maintaining critical services by ensuring resources and access to services are found to be a priority for reducing the overall impact. CONCLUSION: A number of models were used to assess the potential impact of disruptions to essential health services on various outcomes. There is a need for collaboration among stakeholders to enhance the usefulness of any modelling. Future studies should consider disparity issues for more comprehensive findings that could ultimately facilitate policy decision-making to maximise benefits to all.

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., age) and external community (e.g., non-VA provider supply) factors that influence VA enrollment, this study focused on modifiable facility access and quality factors that could influence Veterans' decisions to enroll in VA. In this cohort study, we examined enrollment in and use of VA services in the year following military separation as the binary outcome using mixed-effects logistic regressions, stratified by Active and Reserve Components. This study included 260,777 Active and 101,572 Reserve Component post-9/11 Veterans separated from the military in fiscal years 2016 to 2017. Independent variables included 4 access measures for timeliness of VA care and 3 VA quality measures, which are included in VA Medical Centers' performance plans. Eligible Veterans were more likely to enroll in VA when the closest VA had higher quality scores. After accounting for timeliness of VA care and non-modifiable characteristics, rating of primary care (PC) providers was associated with higher VA enrollment for Active Component (odds ratio [OR] = 1.014, 95% confidence interval [CI]: 1.007-1.020). Higher mental health (MH) continuity (OR = 1.039, 95% CI: 1.000-1.078) and rating of PC providers (OR = 1.009, 95% CI: 1.000-1.017) were associated with higher VA enrollment for Reserve Component. Improving facility-specific quality of care may be a way to increase VA enrollment. In a changing policy environment, study results will help VA leadership target changes they can make to manage enrollment of Veterans in VA and deliver needed foundational services.

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. Multivariable mixed-effect logistic models were implemented to evaluate the association between assay positivity and sex on a low prevalence sample while controlling for age, race, ethnicity, diabetes, cardiovascular disease, hypertension, immunosuppressive therapy, and autoimmune disease. Results: EI positivity among males was 2.1-fold that of females; however, no significant differences in Abbott positivity were observed between sexes. At the manufacturer-recommended threshold, the phi correlation coefficient for the Abbott and EI qualitative results among females (Φ = 0.47) was 34% greater than males (Φ = 0.35). The unadjusted and fully adjusted models yielded a strong association between sex and positive EI result for the low prevalence subgroup (unadjusted OR: 2.24, CI: 1.63, 3.11, adjusted OR: 3.40, CI: 2.15, 5.39). A similar analysis of Abbott positivity in the low prevalence subgroup did not find an association with any of the covariates examined. Significant quantitative and qualitative discordance was observed between Abbott and EI throughout the seroprevalence study. Our results suggest the presence of sex-associated specificity limitations with the EI assay. As these findings may extend to other anti-S assays utilized for SARS-CoV-2 seroprevalence investigations, further investigation is needed to evaluate the generalizability of these findings.