Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach.

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.

Evaluation of Acebilustat, a Selective Inhibitor of Leukotriene B4 Biosynthesis, for Treatment of Outpatients With Mild-Moderate Coronavirus Disease 2019: A Randomized, Double-Blind, Placebo-Controlled Phase 2 Trial.

BACKGROUND: The vast majority of coronavirus disease 2019 (COVID-19) disease occurs in outpatients where treatment is limited to antivirals for high-risk subgroups. Acebilustat, a leukotriene B4 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/d 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 swab samples 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) for 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, the median duration was 4 days (interquartile range, 3-5 days), and the median number of symptoms was 9 (7-11). Most patients (90%) were vaccinated, with 73% having neutralizing antibodies. A minority of participants (44%; 35% in the acebilustat arm and 53% in placebo) had sustained symptom resolution at day 28 (hazard ratio, 0.6 [95% confidence interval, .34-1.04]; P = .07 favoring placebo). There was no difference in the mean AUC for symptom scores over 28 days (difference in mean AUC, 9.4 [95% confidence interval, -42.1 to 60.9]; P = .72). Acebilustat did not affect viral shedding or symptoms at day 120. CONCLUSIONS: Sustained symptoms through day 28 were common in this low-risk population. Despite this, leukotriene B4 antagonism with acebilustat did not shorten symptom duration in outpatients with COVID-19. Clinical Trials Registration. NCT04662060.

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.

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.

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.

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.

Use of a Connected Glucose Meter and Certified Diabetes Educator Coaching to Decrease the Likelihood of Abnormal Blood Glucose Excursions: The Livongo for Diabetes Program.

BACKGROUND: The Livongo for Diabetes Program offers members (1) a cellular technology-enabled, two-way messaging device that measures blood glucose (BG), centrally stores the glucose data, and delivers messages back to the individual in real time; (2) unlimited BG test strips; and (3) access to a diabetes coaching team for questions, goal setting, and automated support for abnormal glucose excursions. The program is sponsored by at-risk self-insured employers, health plans and provider organizations where it is free to members with diabetes or it is available directly to the person with diabetes where they cover the cost. OBJECTIVE: The objective of our study was to evaluate BG data from 4544 individuals with diabetes who were enrolled in the Livongo program from October 2014 through December 2015. METHODS: Members used the Livongo glucose meter to measure their BG levels an average of 1.8 times per day. We estimated the probability of having a day with a BG reading outside of the normal range (70-180 mg/dL, or 3.9-10.0 mmol/L) in months 2 to 12 compared with month 1 of the program, using individual fixed effects to control for individual characteristics. RESULTS: Livongo members experienced an average 18.4% decrease in the likelihood of having a day with hypoglycemia (BG <70 mg/dL) and an average 16.4% decrease in hyperglycemia (BG >180 mg/dL) in months 2-12 compared with month 1 as the baseline. The biggest impact was seen on hyperglycemia for nonusers of insulin. We do not know all of the contributing factors such as medication or other treatment changes during the study period. CONCLUSIONS: These findings suggest that access to a connected glucose meter and certified diabetes educator coaching is associated with a decrease in the likelihood of abnormal glucose excursions, which can lead to diabetes-related health care savings.

Review of Environmental Impact on the Epigenetic Regulation of Atopic Diseases.

There has been significant increase in the prevalence of atopy over the past decade that cannot be explained by genetic predisposition. Environmental factors including nutrition, the uterine environment, and lifestyle factors are known to play a role in gene expression through epigenetic modifications. In this article, we review the literature on the environmental impact on epigenetic modulation of atopic diseases including asthma, food allergy, eczema, and allergic rhinitis. Recent public release of epigenomic data for hundreds of human tissues provides a powerful resource for further investigation of the molecular basis of atopic diseases.

Short-term IL-1beta blockade reduces monocyte CD11b integrin expression in an IL-8 dependent fashion in patients with type 1 diabetes.

OBJECTIVE: Interleukin 1-beta (IL-1beta) is a major inflammatory cytokine. Blockade of the IL-1beta pathway is therapeutically efficacious in type 2 diabetes, but the mechanistic effects on the immune system are incompletely understood. RESEARCH DESIGN: We administered an IL-1 receptor antagonist, anakinra, to 7 type 1 diabetes patients in order to investigate the immunologic and metabolic effects of this drug. Mechanistic assays were performed before and after drug administration. RESULTS: A novel signature was observed, with reduced serum interleukin 8 (IL-8) levels and reduced CD11b integrin expression on monocytes associated with increased CXCR1 expression. CONCLUSIONS: This set of linked phenotypes suggests that blockade of the IL-1beta pathway results in the reduced ability of mononuclear cells to traffic to sites of inflammation. Mechanistic studies from large scale trials using IL-1 blockade in type 1 diabetes should focus on changes in monocyte trafficking and the IL-8 pathway.