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. METHODS: An SPSP was developed and implemented for all clinical and administrative personnel in the Department of Anesthesiology and Perioperative Medicine in three hospitals and six outpatient surgery centers in December 2017. The program incorporated the Scott Three-Tiered Interventional Model of Second Victim Support. Surveys were offered to clinicians in the department prior to implementation of the SPSP and again 18 months after implementation. Among the subset of respondents who experienced a serious adverse patient event, the authors used multiple logistic regression models that adjusted for role and number of night shifts per month to examine differences in perceived resource availability and post-event support received following implementation of the program. RESULTS: There were 94 surveys (83 complete; 11 partially complete) collected prior to implementation and 84 surveys (67 complete; 17 partially complete) collected after implementation. A total of 25 individuals took the survey at both pre and post (19 complete). After implementation, 62.5% of respondents indicated that institutional support had improved since the occurrence of their serious adverse patient event. Statistical models identified a significant improvement in the probability that a clinician agreed with the statement "I think that the organization learned from the event and took appropriate steps to reduce the chance of it happening again" at post vs. pre (adjusted odds ratio [aOR] 3.9, 95% confidence interval [CI] 1.01-15.1. A statistically significant increase from pre to post in the perceived availability of formal emotional support was identified (aOR 5.2, 95% CI 1.9-22.5). CONCLUSION: Implementation of a skilled peer support program within a large department of anesthesiology can improve institutional-based emotional support.

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. As implementation continues and more data are collected and assessed, we also require analysis methods that ensure low statistical error rates as multiple comparisons are made over time. This paper describes how to create a statistical analysis plan for evaluating adaptations to an intervention during ongoing implementation. This can be done by combining methods commonly used in platform clinical trials with methods used for real-world data. We also demonstrate how to use simulations based on previous data to decide the frequency with which to conduct statistical analyses. The illustration uses data from large-scale implementation of a school-based resilience and skill-building preventive intervention to which several adaptations were made. The proposed statistical analysis plan for evaluating the school-based intervention has potential to improve population-level outcomes as implementation scales up further and additional adaptations are anticipated.

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. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

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. These data may identify disparities in COVID-19 outcomes between broad population subgroups, but do not provide comparisons between more granular population subgroups defined by combinations of multiple demographics. We introduce a method that helps to overcome the limitations of aggregated summary statistics and yields estimates of COVID-19 infection and case fatality rates - key quantities for guiding public policy related to the control and prevention of COVID-19 - for population subgroups across combinations of demographic characteristics. Our approach uses pseudo-likelihood based logistic regression to combine aggregate COVID-19 case and fatality data with population-level demographic survey data to estimate infection and case fatality rates for population subgroups across combinations of demographic characteristics. We illustrate our method on California COVID-19 data to estimate test-based infection and case fatality rates for population subgroups defined by gender, age, and race/ethnicity. Our analysis indicates that in California, males have higher test-based infection rates and test-based case fatality rates across age and race/ethnicity groups, with the gender gap widening with increasing age. Although elderly infected with COVID-19 are at an elevated risk of mortality, the test-based infection rates do not increase monotonically with age. The workforce population, especially, has a higher test-based infection rate than children, adolescents, and other elderly people in their 60-80. LatinX and African Americans have higher test-based infection rates than other race/ethnicity groups. The subgroups with the highest 5 test-based case fatality rates are all-male groups with race as African American, Asian, Multi-race, LatinX, and White, followed by African American females, indicating that African Americans are an especially vulnerable California subpopulation.