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The heart transplantation policy change (PC) has improved outcomes in high-acuity (Old 1A, New 1-3) patients, but the effect on low-priority (Old 1B/2, New 4-6) patients is unknown. We sought to determine if low-priority patient outcomes were compromised by benefits to high-priority patients by evaluating for interaction between PC and priority status (PS). We included adult first-time heart transplant candidates and recipients from the UNOS registry during a 19-month period before and after the PC. We compared clinical characteristics and performed competing risks and survival analyses stratified by PC and PS. There was a dependence of PC and PS on waitlist death/deterioration with an interaction sub-distribution hazard ratio (adjusted sdHR) of 0.59 (0.45-0.78), p-value .001. There was a trend toward a benefit of PC on waitlist death/deterioration (adjusted sdHR: 0.86 [0.73-1.01]; p = .07) and an increase in heart transplantation (adjusted sdHR: 1.08 [1.02-1.14], p = .007) for low-priority patients. There was no difference in 1-year post-transplant survival (log-rank p = .22) when stratifying by PC and PS. PC did not negatively affect waitlisted or transplanted low-priority patients. High-priority, post-PC patients had a targeted reduction in waitlist death/deterioration and did not come at the expense of worse post-transplant survival.

This paper rigorously studies large sample properties of a surrogate L 0 penalization method via iteratively performing reweighted L 2 penalized regressions for generalized linear models and develop a scalable implementation of the method for sparse high dimensional massive sample size (sHDMSS) data. We show that for generalized linear models, the limit of the algorithm, referred to as the broken adaptive ridge (BAR) estimator, is consistent for variable selection, enjoys an oracle property for parameter estimation, and possesses a grouping property for highly correlated covariates. We further demonstrate that by taking advantage of an existing efficient implementation of massive L 2 -penalized generalized linear models, the proposed BAR method can be conveniently implemented for sHDMSS data. An illustration is given using a large sHDMSS data from the Truven MarketScan Medicare (MDCR) database to investigate the safety of dabigatran versus warfarin for treatment of nonvalvular atrial filbrillation in elder patients.

Purpose The aim of the study was to determine the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in a university student population. Methods This was a cross-sectional survey study based on the World Health Organization population-based seroepidemiological investigational protocol for SARS-CoV-2 conducted between April 29, 2020, and May 8, 2020, examining SARS-CoV-2 antibody prevalence among 790 university students in Los Angeles, CA. Participants completed a questionnaire on potential risk factors before blood sampling. Samples were analyzed using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) for the qualitative detection of IgG class antibodies to SARS-CoV-2 in human serum or plasma. Results The estimated prevalence of SARS-CoV-2 antibody was 4.0% (3.0%, 5.1%). Factors associated with having a positive test included history of anosmia and/or loss of taste (95% CI: 1.4–9.6). A history of respiratory symptoms, with or without fever, was not associated with a positive antibody test. Conclusions Prevalence of SARS-CoV-2 antibodies in the undergraduate and graduate student university population was similar to community prevalence.

Although authorized mRNA COVID-19 vaccines (BNT162b2 by BioNTech/Pfizer and mRNA-1273 by Moderna) significantly reduce morbidity and mortality, recent evidence suggests that immunity wanes over time, and that a booster dose could further reduce COVID-19 transmission and severe illness. However, research examining attitudes on booster willingness in diverse populations is needed. This study examined COVID-19 booster vaccine attitudes and behaviors among university students and staff in the fall of 2021. In our sample, 96.2% of respondents indicated willingness to get a COVID-19 booster shot at least once per year. In both bivariate and multivariate analyses higher trust in science was associated with having higher odds of booster willingness. Those who identify as Black, on average, reported trusting science less than other racial/ethnic groups. Our findings demonstrate high willingness to receive a COVID-19 booster shot and highlight the importance of educational messages and initiatives that focus on building trust in science to increase willingness to get the COVID-19 booster. More research is needed to better understand the impact of cultural beliefs on booster willingness and vaccine hesitancy. This understanding will help determine what messages and populations to target to increase booster willingness in the future.

This study examined characteristics associated with being unvaccinated among a sample of university staff and faculty prior to university campus reopening for in-person learning in 2021.Staff and faculty responded to an email invitation to complete an online survey. Survey questions included demographic data (race/ethnicity, age, sex), COVID-19 knowledge and behaviors, employment specific data including division and subdivision (healthcare vs. non-healthcare related division); and self-reported vaccination status. A multivariable logistic regression analysis was performed to determine significant characteristics associated with the likelihood of being unvaccinated for COVID-19.Participants identifying as Asian and Asian American (aOR = 1.44, 95% CI: 1.06, 1.96), Hispanic/Latinx (aOR = 1.73, 95% CI: 1.21, 2.49) or Multicultural/Other (aOR = 1.72, 95% CI: 1.24, 2.38) had greater odds of being unvaccinated compared to Non-Hispanic White participants. Other characteristics associated with greater likelihood of being unvaccinated included working as a university staff member (vs. faculty) (aOR = 1.69, 95% CI: 1.24. 2.30), decrease in income (aOR = 1.34, 95% CI:1.05, 1.71), inability to work remotely (aOR = 1.48, 95% CI:1.13, 1.93) and not traveling outside of the Los Angeles area (aOR = 1.46, 95% CI: 1.16, 1.83). Political affiliation as an Independent (aOR = 1.39, 95% CI:1.04, 1.85) or as something else (aOR = 3.84, 95% CI: 2.72, 5.41) were more likely to be unvaccinated compared to participants identifying as Democrat.Several factors associated with racial and social disparities may delay the uptake of COVID-19 vaccination. This study highlights the need for targeted educational interventions to promote vaccination among university staff and faculty.