Coronavirus disease 2019 vaccination is protective of clinical disease in solid organ transplant recipients.

BACKGROUND: Clinical effectiveness of coronavirus disease 2019 (COVID-19) vaccination in solid organ transplant recipients (SOTRs) is not well documented despite multiple studies demonstrating sub-optimal immunogenicity. METHODS: We reviewed medical records of eligible SOTRs at a single center to assess vaccination status and identify cases of symptomatic COVID-19 from January 1 to August 12, 2021. We developed a Cox proportional hazards model using the date of vaccination and time since transplantation as a time-varying covariate with age and gender as potential time-invariant confounders. Survival curves were created using the parameters estimated from the Cox model. RESULTS: Among 1904 SOTRs, 1362 were fully vaccinated (96% received mRNA vaccines) and 542 were either unvaccinated (n = 470) or partially vaccinated (n = 72). There were 115 cases of COVID-19, of which 12 occurred in fully vaccinated individuals. Cox regression with the date of vaccination and time since transplantation as the time-varying co-variates showed that after baseline adjustment for age and sex, being fully vaccinated had a significantly lower hazard for COVID-19, hazard ratio (HR) = 0.29 and 95% confidence interval ([CI] 0.09, 0.91). CONCLUSION: We found that 2-dose mRNA COVID-19 vaccination was protective of symptomatic COVID-19 in vaccinated versus unvaccinated SOTRs. TWEET: COVID-19 vaccination was associated with a significantly lower hazard for symptomatic COVID-19 (HR 0.29; 95% CI 0.09, 0.91) among 1904 SOT recipients at a single center from January 1 to August 12, 2021.

Evaluation of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Mitigation Strategies on a University Campus Using an Agent-Based Network Model.

Universities are faced with decisions on how to resume campus activities while mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce the basic reproduction number, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.

Accounting for Confounding by Time, Early Intervention Adoption, and Time-Varying Effect Modification in the Design and Analysis of Stepped-Wedge Designs: Application to a Proposed Study Design to Reduce Opioid-Related Mortality.

Background: Stepped-wedge designs (SWDs) are currently being used in the investigation of interventions to reduce opioid-related deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and COVID-19 social distancing mandates. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods: We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of components of the intervention, and time-varying effect modificationâ€" in which external factors differentially impact intervention and control clusters. Results: In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions: Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects; misspecification can result in bias of the intervention effect estimate, under coverage of confidence intervals, and Type 1 error inflation. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to choosing appropriate models that account for potential external factors.

Accounting for external factors and early intervention adoption in the design and analysis of stepped-wedge designs: Application to a proposed study design to reduce opioid-related mortality.

BACKGROUND: Stepped-wedge designs (SWDs) are currently being used to investigate interventions to reduce opioid overdose deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and social distancing orders due to the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the proposed intervention as they become widely available. These types of events induce confounding of the intervention effect by time. Such confounding is a well-known limitation of SWDs; a common approach to adjusting for it makes use of a mixed effects modeling framework that includes both fixed and random effects for time. However, these models have several shortcomings when multiple confounding factors are present. METHODS: We discuss the limitations of existing methods based on mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce mortality associated with the opioid epidemic, and propose solutions to accommodate deviations from assumptions that underlie these models. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models under different sources of confounding. We specifically examine the impact of factors external to the study and premature adoption of intervention components. RESULTS: When only external factors are present, our simulation studies show that commonly used mixed effects models can result in unbiased estimates of the intervention effect, but have inflated Type 1 error and result in under coverage of confidence intervals. These models are severely biased when confounding factors differentially impact intervention and control clusters; premature adoption of intervention components is an example of this scenario. In these scenarios, models that incorporate fixed intervention-by-time interaction terms and an unstructured covariance for the intervention-by-cluster-by-time random effects result in unbiased estimates of the intervention effect, reach nominal confidence interval coverage, and preserve Type 1 error, but may reduce power. CONCLUSIONS: The incorporation of fixed and random time effects in mixed effects models require certain assumptions about the impact of confounding by time in SWD. Violations of these assumptions can result in severe bias of the intervention effect estimate, under coverage of confidence intervals, and inflated Type 1 error. Since model choice has considerable impact on study power as well as validity of results, careful consideration needs to be given to choosing an appropriate model that takes into account potential confounding factors.