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Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

BackgroundPreventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection.MethodsWe conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models.ResultsA total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%-1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11-11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8-17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3-4.8). More cases were identified in HCWs when community case rates were high.ConclusionsWe observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.

Introduction: Emerging data suggest that the COVID-19 pandemic has disproportionately impacted women in academic medicine, potentially eliminating recent gains that have been made toward gender equity. This study examined possible pandemic-related gender disparities in research grant submissions, one of the most important criteria for academic promotion and tenure evaluations. Methods: Data were collected from two major academic institutions (one private and one public) on the gender and academic rank of faculty principal investigators who submitted new grants to the National Institutes of Health (NIH) during COVID-19 (March 1st, 2020, through August 31, 2020) compared with a matched period in 2019 (March 1st, 2019, through August 31, 2019). t-Tests and chi-square analyses compared the gender distribution of individuals who submitted grants during the two periods of examination. Results: In 2019 (prepandemic), there was no significant difference in the average number of grants submitted by women compared with men faculty. In contrast, women faculty submitted significantly fewer grants in 2020 (during the pandemic) than men. Men were also significantly more likely than women to submit grants in both 2019 and 2020 compared with submitting in 2019 only, suggesting men faculty may have been more likely than their women colleagues to sustain their productivity in grant submissions during the pandemic. Discussion: Women's loss of extramural funding may compound over time, as it impedes new data collection, research progress, and academic advancement. Efforts to support women's research productivity and career trajectories are urgently needed in the following years of pandemic recovery.

A damaging inflammatory response is implicated in the pathogenesis of severe coronavirus disease 2019 (COVID-19), but mechanisms contributing to this response are unclear. In two prospective cohorts, early non-neutralizing, afucosylated immunoglobulin G (IgG) antibodies specific to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were associated with progression from mild to more severe COVID-19. To study the biology of afucosylated IgG immune complexes, we developed an in vivo model that revealed that human IgG-Fc-gamma receptor (FcγR) interactions could regulate inflammation in the lung. Afucosylated IgG immune complexes isolated from patients with COVID-19 induced inflammatory cytokine production and robust infiltration of the lung by immune cells. In contrast to the antibody structures that were associated with disease progression, antibodies that were elicited by messenger RNA SARS-CoV-2 vaccines were highly fucosylated and enriched in sialylation, both modifications that reduce the inflammatory potential of IgG. Vaccine-elicited IgG did not promote an inflammatory lung response. These results show that human IgG-FcγR interactions regulate inflammation in the lung and define distinct lung activities mediated by the IgG that are associated with protection against, or progression to, severe COVID-19.

PurposeWe describe the design of a longitudinal cohort study to determine SARS-CoV-2 incidence and prevalence among a population-based sample of adults living in six San Francisco Bay Area counties.MethodsUsing an address-based sample, we stratified households by county and by census-tract risk. Risk strata were determined by using regression models to predict infections by geographic area using census-level sociodemographic and health characteristics. We disproportionately sampled high and medium risk strata, which had smaller population sizes, to improve precision of estimates, and calculated a desired sample size of 3400. Participants were primarily recruited by mail and were followed monthly with PCR testing of nasopharyngeal swabs, testing of venous blood samples for antibodies to SARS-CoV-2 spike and nucleocapsid antigens, and testing of the presence of neutralizing antibodies, with completion of questionnaires about socio-demographics and behavior. Estimates of incidence and prevalence will be weighted by county, risk strata and sociodemographic characteristics of non-responders, and will take into account laboratory test performance.ResultsWe enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata.ConclusionsOur stratified random sampling design will allow us to recruit a robust general population cohort of adults to determine the incidence of SARS-CoV-2 infection. Identifying risk strata was unique to the design and will help ensure precise estimates, and high-performance testing for presence of virus and antibodies will enable accurate ascertainment of infections.

BackgroundThe great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients.MethodsLeveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial.ResultsWe identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer-BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2-7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset.ConclusionsEarly immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models.FundingSupport for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford's Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.

Since the first case of COVID-19 was identified in the USA in January, 2020, over 46 million people in the country have tested positive for SARS-CoV-2 infection. Several COVID-19 vaccines have received emergency use authorisations from the US Food and Drug Administration, with the Pfizer-BioNTech vaccine receiving full approval on Aug 23, 2021. When paired with masking, physical distancing, and ventilation, COVID-19 vaccines are the best intervention to sustainably control the pandemic. However, surveys have consistently found that a sizeable minority of US residents do not plan to get a COVID-19 vaccine. The most severe consequence of an inadequate uptake of COVID-19 vaccines has been sustained community transmission (including of the delta [B.1.617.2] variant, a surge of which began in July, 2021). Exacerbating the direct impact of the virus, a low uptake of COVID-19 vaccines will prolong the social and economic repercussions of the pandemic on families and communities, especially low-income and minority ethnic groups, into 2022, or even longer. The scale and challenges of the COVID-19 vaccination campaign are unprecedented. Therefore, through a series of recommendations, we present a coordinated, evidence-based education, communication, and behavioural intervention strategy that is likely to improve the success of COVID-19 vaccine programmes across the USA.

The study was designed to compare intentions to receive COVID-19 vaccination by race-ethnicity, to identify beliefs that may mediate the association between race-ethnicity and intention to receive the vaccine and to identify the demographic factors and beliefs most strongly predictive of intention to receive a vaccine. Cross-sectional survey conducted from November 2020 to January 2021, nested within a longitudinal cohort study of the prevalence and incidence of SARS-CoV-2 among a general population-based sample of adults in six San Francisco Bay Area counties (called TrackCOVID). Study Cohort: In total, 3161 participants among the 3935 in the TrackCOVID parent cohort responded. Rates of high vaccine willingness were significantly lower among Black (41%), Latinx (55%), Asian (58%), Multi-racial (59%), and Other race (58%) respondents than among White respondents (72%). Black, Latinx, and Asian respondents were significantly more likely than White respondents to endorse lack of trust of government and health agencies as a reason not to get vaccinated. Participants' motivations and concerns about COVID-19 vaccination only partially explained racial-ethnic differences in vaccination willingness. Concerns about a rushed government vaccine approval process and potential bad reactions to the vaccine were the two most important factors predicting vaccination intention. Vaccine outreach campaigns must ensure that the disproportionate toll of COVID-19 on historically marginalized racial-ethnic communities is not compounded by inequities in vaccination. Efforts must emphasize messages that speak to the motivations and concerns of groups suffering most from health inequities to earn their trust to support informed decision making.

BackgroundThere is limited information on perinatal outcomes in HIV-hepatitis B virus (HBV) coinfection.MethodsHIV Prevention Trials Network (HPTN) 046 was a randomized double-blind placebo-controlled trial of perinatal transmission that evaluated 6 months of infant nevirapine versus placebo among breast-fed infants. Women living with HIV and their infants enrolled in sub-Saharan Africa from 2007 to 2010; 78% received antiretroviral therapy (ART). Maternal samples were tested for hepatitis B surface antigen (HBsAg). High and low HBV viral load (VL) was defined as ≥106 IU/mL and

The potential role of enteric viral infections and the developing infant virome in affecting immune responses to the oral poliovirus vaccine (OPV) is unknown. Here we performed viral metagenomic sequencing on 3 serially collected stool samples from 30 Bangladeshi infants following OPV vaccination and compared findings to stool samples from 16 age-matched infants in the United States (US). In 14 Bangladeshi infants, available post-vaccination serum samples were tested for polio-neutralizing antibodies. The abundance (p = 0.006) and richness (p = 0.013) of the eukaryotic virome increased with age and were higher than seen in age-matched US infants (p

Introduction: Hospitals commonly use Press Ganey (PG) patient satisfaction surveys forbenchmarking physician performance. PG scores range from 1 to 5, with 5 being the highest,which is known as the “topbox” score. Our objective was to identify patient and physician factorsassociated with topbox PG scores in the emergency department (ED).Methods: We looked at PG surveys from January 2015–December 2017 at an academic, urbanhospital with 78,000 ED visits each year. Outcomes were topbox scores for the questions:“Likelihood of your recommending our ED to others”; and “Courtesy of the doctor.” We analyzedtopbox scores using generalized estimating equation models clustered by physician and adjustedfor patient and physician factors. Patient factors included age, gender, race, ethnicity, and ED areawhere patient was seen. The ED has four areas based on patient acuity: emergent; urgent; vertical(urgent but able to sit in a recliner rather than a gurney); and fast track (non-urgent). Physicianfactors included age, gender, race, ethnicity, and number of years at current institution.Results: We analyzed a total of 3,038 surveys. For “Likelihood of your recommending our ED toothers,” topbox scores were more likely with increasing patient age (odds ratio [OR] 1.07; 95%confidence interval [CI], 1.03-1.12); less likely among female compared to male patients (OR 0.81;95% CI, 0.70-0.93); less likely among Asian compared to White patients (OR 0.71; 95% CI, 0.60-0.83); and less likely in the urgent (OR 0.71; 95% CI, 0.54-0.93) and vertical areas (OR 0.71; 95% CI0.53-0.95) compared to fast track. For “Courtesy of the doctor,” topbox scores were more likely withincreasing patient age (OR 1.1; CI, 1.06-1.14); less likely among Asian (OR 0.70; 95% CI, 0.58-0.84),Black (OR 0.66; 95% CI ,0.45-0.96), and Hispanic patients (OR 0.68; 95% CI ,0.55-0.83) compared toWhite patients; and less likely in urgent area (OR 0.69; 95% CI ,0.50-0.95) compared to fast track.Conclusion: Increasing patient age was associated with increased likelihood of topbox scores,while Asian patients, and urgent and vertical areas had decreased likelihood of topbox scores. Weencourage hospitals that use PG topbox scores as financial incentives to understand the contributionof non-service factors to these scores.

Background With the future epidemiology and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uncertain, the use of safe and effective coronavirus disease 2019 (COVID-19) vaccines in pediatric populations remains important. Methods We report data from two open-label substudies of an ongoing phase 1/2/3 master study (NCT05543616) investigating the safety and immunogenicity of a variant-adapted bivalent COVID-19 vaccine encoding ancestral and Omicron BA.4/BA.5 spike proteins (bivalent BNT162b2). The open-label groups presented here evaluate dose 4 with bivalent BNT162b2 in 6-month- to <12-year-olds who previously received three original (monovalent) BNT162b2 doses. In 6-month- to <5-year-olds, primary immunogenicity objectives were to demonstrate superiority (neutralizing titer) and noninferiority (seroresponse rate) to Omicron BA.4/BA.5 and noninferiority (neutralizing titer and seroresponse rate) to SARS-CoV-2 ancestral strains in participants who received bivalent BNT162b2 dose 4 compared with a matched group who received three doses of original BNT162b2 in the pivotal pediatric study (NCT04816643). In 5- to <12-year-olds, primary immunogenicity comparisons were descriptive. Reactogenicity and safety following vaccination were evaluated. Results In 6-month- to <5-year-olds, dose 4 with bivalent BNT162b2 met predefined immunogenicity superiority and noninferiority criteria against Omicron BA.4/BA.5 and ancestral strains when compared with dose 3 of original BNT162b2. In 5- to <12-year-olds, bivalent BNT162b2 induced robust Omicron BA.4/BA.5 and ancestral strain neutralizing titers comparable with dose 3 of original BNT162b2. The safety profile for dose 4 of bivalent BNT162b2 given as dose 4 was consistent with that of original BNT162b2 in 6-month- to <12-year-olds. Reactogenicity events were generally mild to moderate. No adverse events led to discontinuation. Conclusions These safety and immunogenicity data support a favorable benefit-risk profile for a variant-adapted BNT162b2 in children <12 years old. [ABSTRACT FROM AUTHOR]

Importance: The COVID-19 pandemic has led to 775 million documented cases and over 7 million deaths worldwide as of March 2024 and is an ongoing health crisis. To limit viral spread within households and in the community, public health officials have recommended self-isolation, self-quarantine of exposed household contacts, and mask use. Yet, risk of household transmission (HHT) may be underestimated due to low frequency of sampling, and risk factors for HHT are not well understood. Objectives: To estimate the secondary attack rate of SARS-CoV-2 within households and to define the risk factors for new infections in household members who are in close contact with the index case. Design, setting, and participants: In this prospective cohort study, from March 2020—December 2021 we enrolled 60 households with index cases who tested positive for SARS-CoV-2. All household contacts and index cases were tested daily for SARS-CoV-2 via reverse transcription polymerase chain reaction (RT-PCR) using self-collected anterior nares specimens. Households were followed until all study participants in the household tested negative for SARS-CoV-2 for seven consecutive days. We collected sex, age, race/ethnicity, comorbidities, and relationship to index case for secondary contacts, household level characteristics including primary income, household density, and square feet per person on property. We compared the sociodemographic variables between COVID-19 positive and negative household members and between households where secondary transmission did and did not occur. Main outcomes and measures: Daily anterior nares swabs were tested for SARS-CoV-2 using RT-PCR, in order to assess duration of nasal shedding of SARS-CoV-2, as well as risk of transmission to secondary household contacts. Results: Of the 163 participants in this study, 84 (51.5%) were women; median age (IQR) was 36.0 (17.0–54.0) years of age; 78 (47.8%) were white and 48 (29.5%) were Hispanic/LatinX. Of the fifty households with household contacts, at least one secondary case occurred in twenty-six households (52.0%) and forty-five household contacts (43.7%) were infected. Secondary attack rate was lowest among children of index cases (6/23, 26.1%). Modified Poisson regression identified that the risk of transmission to household contacts increases significantly with age (Risk ratio for each increase in years of age = 1.01, 95% CI = 1.00–1.02). Mixed effects regression models identified that participants with chronic diseases, such as asthma, diabetes, cancer, or cardiac disease, had higher Cts at baseline when compared to participants without chronic diseases (6.62, 95% CI: 1.46–11.77, p = 0.02) and show a slower rate of increase in Ct over time (-0.43, 95% CI: -0.77 to -0.09, p = 0.02) Conclusions and relevance: This study suggests that HHT represents a key source of community-based infection of SARS-CoV-2. Allocation of resources for contact investigations and prevention interventions should focus on the individuals at highest risk of infection in households, especially those with higher density homes. [ABSTRACT FROM AUTHOR]

Background Nirmatrelvir/ritonavir (N/R) reduces severe outcomes from coronavirus disease 2019 (COVID-19); however, rebound after treatment has been reported. We compared symptom and viral dynamics in individuals with COVID-19 who completed N/R treatment and similar untreated individuals. Methods We identified symptomatic participants who tested severe acute respiratory syndrome coronavirus 2–positive and were N/R eligible from a COVID-19 household transmission study. Index cases from ambulatory settings and their households contacts were enrolled. We collected daily symptoms, medication use, and respiratory specimens for quantitative polymerase chain reaction for 10 days during March 2022—May 2023. Participants who completed N/R treatment (treated) were propensity score matched to untreated participants. We compared symptom rebound, viral load (VL) rebound, average daily symptoms, and average daily VL by treatment status measured after N/R treatment completion or 7 days after symptom onset if untreated. Results Treated (n = 130) and untreated participants (n = 241) had similar baseline characteristics. After treatment completion, treated participants had greater occurrence of symptom rebound (32% vs 20%; P =.009) and VL rebound (27% vs 7%; P <.001). Average daily symptoms were lower among treated participants without symptom rebound (1.0 vs 1.6; P <.01) but not statistically lower with symptom rebound (3.0 vs 3.4; P =.5). Treated participants had lower average daily VLs without VL rebound (0.9 vs 2.6; P <.01) but not statistically lower with VL rebound (4.8 vs 5.1; P =.7). Conclusions Individuals who completed N/R treatment experienced fewer symptoms and lower VL but rebound occured more often compared with untreated individuals. Providers should prescribe N/R, when indicated, and communicate rebound risk to patients. [ABSTRACT FROM AUTHOR]

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectationmaximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings. [ABSTRACT FROM AUTHOR]