Department of Anesthesiology Skilled Peer Support Program Outcomes: Second Victim Perceptions.

Background: Most anesthesia providers experience an adverse event during their training or career. Limited evidence suggests skilled peer support programs (SPSPs) reduce initial distress and support adaptive functioning and coping. This study evaluated second victim perceptions of a voluntary SPSP.

Creating a Statistical Analysis Plan to Continually Evaluate Intervention Adaptations that Arise in Real-World Implementation.

Evidence-based health interventions are frequently translated into real-world settings where practical needs drive changes to intervention protocols. Due to logistical and resource constraints, these naturally arising adaptations are rarely assessed for comparative effectiveness using a randomized trial. Nevertheless, when observational data are available, it is still possible to identify beneficial adaptations using statistical methods that adjust for differences among intervention groups.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions.

Pseudo-likelihood based logistic regression for estimating COVID-19 infection and case fatality rates by gender, race, and age in California.

In emerging epidemics, early estimates of key epidemiological characteristics of the disease are critical for guiding public policy. In particular, identifying high-risk population subgroups aids policymakers and health officials in combating the epidemic. This has been challenging during the coronavirus disease 2019 (COVID-19) pandemic because governmental agencies typically release aggregate COVID-19 data as summary statistics of patient demographics.