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In an analysis involving more than 10 million North Carolina residents, Covid-19 vaccines were highly effective in preventing hospitalization and death up to 9 months after vaccination. Waning protection against infection over time was due to both declining immunity and the emergence of the delta variant.

Effectiveness of Bivalent Covid-19 BoostersIn this study, effectiveness against hospitalization or death was 24.9% after a monovalent booster and 61.8% after a bivalent booster. Protection has waned over time.

In a trial of two bivalent Covid-19 boosters, effectiveness against severe infection resulting in hospitalization or death reached a level of 67.4% after 2 weeks and decreased to 38.4% after 20 weeks.

A data survey of 1.8 million people in Nebraska showed that XBB.1.5 vaccine effectiveness was 52% against infection and 67% against hospitalization, despite a decline in circulating XBB.1.5 during data collection.

In a 6-month study in children while omicron was dominant, previous SARS-CoV-2 infection and the BNT162b2 vaccine lowered the risks of infection, hospitalization, and death, but protection against omicron declined rapidly.

Partly interval-censored (PIC) data arise when some failure times are exactly observed while others are only known to lie within certain intervals. In this article, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with PIC data. We first generalize the Buckley-James estimator for right-censored data to PIC data. Then, we develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We show that under mild regularity conditions the generalized Buckley-James estimator is consistent and asymptotically normal and the one-step estimator is consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. We conduct extensive simulation studies to examine the performance of the proposed estimators in finite samples and apply our methods to data derived from an AIDS study.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

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. 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. 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. 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. US National Institutes of Health.

In modern epidemiological and clinical studies, the covariates of interest may involve genome sequencing, biomarker assay, or medical imaging and thus are prohibitively expensive to measure on a large number of subjects. A cost-effective solution is the two-phase design, under which the outcome and inexpensive covariates are observed for all subjects during the first phase and that information is used to select subjects for measurements of expensive covariates during the second phase. For example, subjects with extreme values of quantitative traits were selected for whole-exome sequencing in the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP). Herein, we consider general two-phase designs, where the outcome can be continuous or discrete, and inexpensive covariates can be continuous and correlated with expensive covariates. We propose a semiparametric approach to regression analysis by approximating the conditional density functions of expensive covariates given inexpensive covariates with B-spline sieves. We devise a computationally efficient and numerically stable EM-algorithm to maximize the sieve likelihood. In addition, we establish the consistency, asymptotic normality, and asymptotic efficiency of the estimators. Furthermore, we demonstrate the superiority of the proposed methods over existing ones through extensive simulation studies. Finally, we present applications to the aforementioned NHLBI ESP. Supplementary materials for this article are available online