Impact of booster vaccination interval on SARS-CoV-2 infection, hospitalization, and death.

OBJECTIVES: We investigated how booster interval affects the risks of SARS-CoV-2 infection and Covid-19-related hospitalization and death in different age groups. METHODS: We collected data on booster receipts and Covid-19 outcomes between September 22, 2021 and February 9, 2023 for 5,769,205 North Carolina residents ≥12 years of age who had completed their primary vaccination series. We related Covid-19 outcomes to baseline characteristics and booster doses through Cox regression models. RESULTS: For adults ≥65 years of age, boosting every 9 months was associated with proportionate reductions (compared with no boosting) of 18.9% (95% CI, 18.5-19.4) in the cumulative frequency of infection, 37.8% (95% CI, 35.3-40.3) in the cumulative risk of hospitalization or death, and 40.9% (95% CI, 37.2-44.7) in the cumulative risk of death at 2 years after completion of primary vaccination. The reductions were lower by boosting every 12 months and higher by boosting every 6 months. The reductions were smaller for individuals 12-64 years of age. CONCLUSION: Boosting at a shorter interval was associated with a greater reduction in Covid-19 outcomes, especially hospitalization and death. Frequent boosting conferred greater benefits for individuals aged ≥65 than for individuals aged 12-64.

Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials.

BACKGROUND: The current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness. METHODS: We specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials. RESULTS: For remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23-1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09-1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10-1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13-1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79-1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85-1.12; p = 0.74). CONCLUSIONS: The proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.

Fitting the Cox proportional hazards model to big data.

The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions. We show that the final estimator has the same asymptotic distribution as the conventional maximum partial likelihood estimator using the whole dataset but requires only a small fraction of computation time. We demonstrate the usefulness of the proposed method through extensive simulation studies and an application to the UK Biobank data.

Proportional rates models for multivariate panel count data.

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.

Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos.

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.

Nirmatrelvir or Molnupiravir Use and Severe Outcomes From Omicron Infections.

Importance: Ritonavir-boosted nirmatrelvir and molnupiravir are currently used in the US and in other countries to treat nonhospitalized patients who have mild-to-moderate COVID-19 and who are at high risk for progression to severe disease. The associations of these 2 oral antiviral drugs with hospitalization and death resulting from infection with new SARS-CoV-2 Omicron subvariants, particularly BQ.1.1 and XBB.1.5, are unknown. Objective: To assess the association of nirmatrelvir or molnupiravir use with the risks of hospitalization and death among patients infected with new Omicron subvariants. Design, Setting, and Participants: This was a cohort study of patients who received a diagnosis of COVID-19 at Cleveland Clinic from April 1, 2022, to February 20, 2023 (during which the Omicron variant evolved from BA.2 to BA.4/BA.5, then to BQ.1/BQ.1.1, and finally to XBB/XBB.1.5) and who were at high risk of progressing to severe disease, with follow-up through 90 days after diagnosis. The final date for follow-up data collection was February 27, 2023. Exposures: Treatment with ritonavir-boosted nirmatrelvir or molnupiravir. Main Outcomes and Measures: The primary outcome was time to death. The secondary outcome was time to either hospitalization or death. The association of either nirmatrelvir or molnupiravir use with each outcome was measured by the hazard ratio (HR) adjusted for demographic factors, socioeconomic status, date of COVID-19 diagnosis, coexisting medical conditions, COVID-19 vaccination status, and previous SARS-CoV-2 infection. Results: There were 68 867 patients (29 386 [42.7%] aged ≥65 years; 26 755 [38.9%] male patients; 51 452 [74.7%] non-Hispanic White patients). Thirty of 22 594 patients treated with nirmatrelvir, 27 of 5311 patients treated with molnupiravir, and 588 of 40 962 patients who received no treatment died within 90 days of Omicron infection. The adjusted HRs of death were 0.16 (95% CI, 0.11-0.23) for nirmatrelvir and 0.23 (95% CI, 0.16-0.34) for molnupiravir. The adjusted HRs of hospitalization or death were 0.63 (95% CI, 0.59-0.68) for nirmatrelvir and 0.59 (95% CI, 0.53-0.66) for molnupiravir. The associations of both drugs with both outcomes were observed across subgroups defined by age, race and ethnicity, date of COVID-19 diagnosis, vaccination status, previous infection status, and coexisting conditions. Conclusions and Relevance: These findings suggest that the use of either nirmatrelvir or molnupiravir is associated with reductions in mortality and hospitalization in patients infected with Omicron, regardless of age, race and ethnicity, virus strain, vaccination status, previous infection status, or coexisting conditions. Both drugs can, therefore, be used to treat nonhospitalized patients who are at high risk of progressing to severe COVID-19.

Effects of COVID-19 vaccination and previous SARS-CoV-2 infection on omicron infection and severe outcomes in children under 12 years of age in the USA: an observational cohort study.

BACKGROUND: Data on the protection conferred by COVID-19 vaccination and previous SARS-CoV-2 infection against omicron (B.1.1.529) infection in young children are scarce. We aimed to estimate the time-varying effects of primary and booster COVID-19 vaccination and previous SARS-CoV-2 infection on subsequent omicron infection and severe illness (hospital admission or death) in children younger than 12 years of age. METHODS: In this observational cohort study, we obtained individual-level records on vaccination with the BNT162b2 and mRNA-1273 vaccines and clinical outcomes from the North Carolina COVID-19 Surveillance System and the COVID-19 Vaccine Management System for 1 368 721 North Carolina residents aged 11 years or younger from Oct 29, 2021 (Oct 29, 2021 for children aged 5-11 years and June 17, 2022 for children aged 0-4 years), to Jan 6, 2023. We used Cox regression to estimate the time-varying effects of primary and booster vaccination and previous infection on the risks of omicron infection, hospital admission, and death. FINDINGS: For children 5-11 years of age, the effectiveness of primary vaccination against infection, compared with being unvaccinated, was 59·9% (95% CI 58·5-61·2) at 1 month, 33·7% (32·6-34·8) at 4 months, and 14·9% (95% CI 12·3-17·5) at 10 months after the first dose. Compared with primary vaccination only, the effectiveness of a monovalent booster dose after 1 month was 24·4% (14·4-33·2) and that of a bivalent booster dose was 76·7% (45·7-90·0). The effectiveness of omicron infection against reinfection was 79·9% (78·8-80·9) after 3 months and 53·9% (52·3-55·5) after 6 months. For children 0-4 years of age, the effectiveness of primary vaccination against infection, compared with being unvaccinated, was 63·8% (57·0-69·5) at 2 months and 58·1% (48·3-66·1) at 5 months after the first dose, and the effectiveness of omicron infection against reinfection was 77·3% (75·9-78·6) after 3 months and 64·7% (63·3-66·1) after 6 months. For both age groups, vaccination and previous infection had better effectiveness against severe illness as measured by hospital admission or death as a composite endpoint than against infection. INTERPRETATION: The BNT162b2 and mRNA-1273 vaccines were effective against omicron infection and severe outcomes in children younger than 12 years, although the effectiveness decreased over time. Bivalent boosters were more effective than monovalent boosters. Immunity acquired via omicron infection was high and waned gradually over time. These findings can be used to develop effective prevention strategies against COVID-19 in children younger than 12 years. FUNDING: US National Institutes of Health.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

Impact of Vaccination, Prior Infection, and Therapy on Omicron Infection and Mortality.

BACKGROUND: Understanding immunity against Omicron infection and severe outcomes conferred by coronavirus disease 2019 (Covid-19) vaccination, prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and monoclonal antibody therapy will inform intervention strategies. METHODS: We considered 295 691 patients tested for SARS-CoV-2 at Cleveland Clinic between 1 October 2021 and 31 January 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection, and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death. RESULTS: Vaccination and prior infection were less effective against Omicron than Delta infection but provided strong protection against ICU admission and death. Boosting greatly increased vaccine effectiveness against Omicron infection and severe outcomes, although effectiveness waned rapidly over time. Monoclonal antibody therapy considerably reduced risks of ICU admission and death. The relatively low mortality of the Omicron variant was due to both reduced lethality of this variant and increased population immunity acquired from booster vaccination and previous infection. CONCLUSIONS: Booster vaccination and prior SARS-CoV-2 infection provide strong protection against ICU admission and death from Omicron infection. Monoclonal antibody therapy is also beneficial.

Mediation analysis of multiple mediators with incomplete omics data.

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes.

Importance: Data about the association of COVID-19 vaccination and prior SARS-CoV-2 infection with risk of SARS-CoV-2 infection and severe COVID-19 outcomes may guide prevention strategies. Objective: To estimate the time-varying association of primary and booster COVID-19 vaccination and prior SARS-CoV-2 infection with subsequent SARS-CoV-2 infection, hospitalization, and death. Design, Setting, and Participants: Cohort study of 10.6 million residents in North Carolina from March 2, 2020, through June 3, 2022. Exposures: COVID-19 primary vaccine series and boosters and prior SARS-CoV-2 infection. Main Outcomes and Measures: Rate ratio (RR) of SARS-CoV-2 infection and hazard ratio (HR) of COVID-19-related hospitalization and death. Results: The median age among the 10.6 million participants was 39 years; 51.3% were female, 71.5% were White, and 9.9% were Hispanic. As of June 3, 2022, 67% of participants had been vaccinated. There were 2 771 364 SARS-CoV-2 infections, with a hospitalization rate of 6.3% and mortality rate of 1.4%. The adjusted RR of the primary vaccine series compared with being unvaccinated against infection became 0.53 (95% CI, 0.52-0.53) for BNT162b2, 0.52 (95% CI, 0.51-0.53) for mRNA-1273, and 0.51 (95% CI, 0.50-0.53) for Ad26.COV2.S 10 months after the first dose, but the adjusted HR for hospitalization remained at 0.29 (95% CI, 0.24-0.35) for BNT162b2, 0.27 (95% CI, 0.23-0.32) for mRNA-1273, and 0.35 (95% CI, 0.29-0.42) for Ad26.COV2.S and the adjusted HR of death remained at 0.23 (95% CI, 0.17-0.29) for BNT162b2, 0.15 (95% CI, 0.11-0.20) for mRNA-1273, and 0.24 (95% CI, 0.19-0.31) for Ad26.COV2.S. For the BNT162b2 primary series, boosting in December 2021 with BNT162b2 had the adjusted RR relative to primary series of 0.39 (95% CI, 0.38-0.40) and boosting with mRNA-1273 had the adjusted RR of 0.32 (95% CI, 0.30-0.34) against infection after 1 month and boosting with BNT162b2 had the adjusted RR of 0.84 (95% CI, 0.82-0.86) and boosting with mRNA-1273 had the adjusted RR of 0.60 (95% CI, 0.57-0.62) after 3 months. Among all participants, the adjusted RR of Omicron infection compared with no prior infection was estimated at 0.23 (95% CI, 0.22-0.24) against infection, and the adjusted HRs were 0.10 (95% CI, 0.07-0.14) against hospitalization and 0.11 (95% CI, 0.08-0.15) against death after 4 months. Conclusions and Relevance: Receipt of primary COVID-19 vaccine series compared with being unvaccinated, receipt of boosters compared with primary vaccination, and prior infection compared with no prior infection were all significantly associated with lower risk of SARS-CoV-2 infection (including Omicron) and resulting hospitalization and death. The associated protection waned over time, especially against infection.