Evaluation of acebilustat, a selective inhibitor of leukotriene B4 biosynthesis, for treatment of outpatients with mild-moderate COVID-19 disease: A randomized, double-blind, placebo- controlled Phase 2 trial.

BACKGROUND: The vast majority of COVID-19 disease occurs in outpatients where treatment is limited to anti-virals for high-risk subgroups. Acebilustat, a leukotriene B4 (LTB4) inhibitor, has potential to reduce inflammation and symptom duration. METHODS: In a single-center trial spanning Delta and Omicron variants, outpatients were randomized to 100 mg of oral acebilustat or placebo for 28 days. Patients reported daily symptoms via electronic query through Day 28 with phone follow-up on Day 120 and collected nasal swabs on Days 1-10. The primary outcome was sustained symptom resolution to Day 28. Secondary 28-day outcomes included time to first symptom resolution, area under the curve (AUC) of longitudinal daily symptom scores; duration of viral shedding through Day 10; and symptoms on Day 120. RESULTS: Sixty participants were randomized to each study arm. At enrollment, median duration and number of symptoms were 4 (IQR 3-5) days and 9 (IQR 7-11) symptoms. Most patients (90%) were vaccinated with 73% having neutralizing antibodies. A minority (44%) of participants (35% in the acebilustat arm and 53% in placebo) had sustained symptom resolution at Day 28 (HR 0.6, 95% CI 0.34-1.04, p = 0.07 favoring placebo). There was no difference in mean AUC of symptom scores over 28 days (difference in mean of AUC 9.4, 95% CI -42.1-60.9, p=0.72). Acebilustat did not impact viral shedding or symptoms at Day 120. CONCLUSIONS: Sustained symptoms through Day 28 were common in this low-risk population. Despite this, LTB4 antagonism with acebilustat did not shorten symptom duration in outpatients with COVID-19.

Successful recruitment of monolingual Spanish speaking Latinos to university phase II and III outpatient COVID-19 clinical treatment trials in Northern California.

Through a public County/University partnership, we employed a Spanish/English bilingual research coordinator to increase awareness of newly available treatments with FDA Emergency Use Authorization and clinical trial opportunities for Latino outpatients with mild to moderate COVID-19. Out of the 550 San Mateo County outpatients with COVID-19 referred to Stanford University between July 2020 and April 2022, 9.5% elected to receive monoclonal antibody EUA treatment. COVID-19 treatment trial enrollment of County patients, 5% of those recruited, was commensurate with non-County populations enrollment. Recruitment models such as ours have the potential to increase US Latino populations' recruitment in outpatient COVID-19 treatment trials and contribute to decreasing COVID-19 health disparities.

Incidence and Prevalence of Coronavirus Disease 2019 Within a Healthcare Worker Cohort During the First Year of the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic.

BACKGROUND: Preventing 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. METHODS: We 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. RESULTS: A 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. CONCLUSIONS: We 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.

Validity of at-home rapid antigen lateral flow assay and artificial intelligence read to detect SARS-CoV-2.

BACKGROUND: The gold standard for COVID-19 diagnosis-reverse-transcriptase polymerase chain reaction (RT-PCR)- is expensive and often slow to yield results whereas lateral flow tests can lack sensitivity. METHODS: We tested a rapid, lateral flow antigen (LFA) assay with artificial intelligence read (LFAIR) in subjects from COVID-19 treatment trials (N = 37; daily tests for 5 days) and from a population-based study (N = 88; single test). LFAIR was compared to RT-PCR from same-day samples. RESULTS: Using each participant's first sample, LFAIR showed 86.2% sensitivity (95% CI 73.6%-98.8) and 94.3% specificity (88.8%-99.7%) compared to RT-PCR. Adjusting for days since symptom onset and repeat testing, sensitivity was 97.8% (89.9%-99.5%) on the first symptomatic day and decreased with each additional day. Sensitivity improved with artificial intelligence (AI) read (86.2%) compared to the human eye (71.4%). CONCLUSION: LFAIR showed improved accuracy compared to LFA alone. particularly early in infection.

Race-ethnicity and COVID-19 Vaccination Beliefs and Intentions: A Cross-Sectional Study among the General Population in the San Francisco Bay Area.

OBJECTIVE: 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. DESIGN: 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. RESULTS: 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. CONCLUSIONS: 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.

Design of a population-based longitudinal cohort study of SARS-CoV-2 incidence and prevalence among adults in the San Francisco Bay Area.

PURPOSE: We 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. METHODS: Using 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. RESULTS: We enrolled 3842 adults from August to December 2020, and completed follow-up March 31, 2021. We reached target sample sizes within most strata. CONCLUSIONS: Our 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.