State-Level Economic Policy Uncertainty

We quantify and study state-level economic policy uncertainty. Tapping digital archives for nearly 3,500 local newspapers, we construct three monthly indexes for each state: one that captures state and local sources of policy uncertainty (EPU−S), one that captures national and international sources (EPU−N), and a composite index that captures both. EPU−S rises around gubernatorial elections and own-state episodes like the California electricity crisis of 2000-01 and the Kansas tax experiment of 2012. EPU−N rises around presidential elections and in response to 9-11, Gulf Wars I and II, the 2011 debt-ceiling crisis, the 2012 fiscal cliff episode, and federal government shutdowns. Close elections elevate policy uncertainty much more than the average election. VAR models fit to pre-COVID data imply that upward shocks to own-state EPU foreshadow weaker economic performance in the state, as do upward EPU shocks in contiguous states. The COVID-19 pandemic drove huge increases in policy uncertainty and unemployment, more so in states with stricter government-mandated lockdowns.

Utilising an Accelerated Delphi Process to Develop Guidance and Protocols for Telepresence Applications in Remote Robotic Surgery Training.

CONTEXT: The role of robot-assisted surgery continues to expand at a time when trainers and proctors have travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: To provide guidance on setting up and running an optimised telementoring service that can be integrated into current validated curricula. We define a standardised approach to training candidates in skill acquisition via telepresence technologies. We aim to describe an approach based on the current evidence and available technologies, and define the key elements within optimised telepresence services, by seeking consensus from an expert committee comprising key opinion leaders in training. EVIDENCE ACQUISITION: This project was carried out in phases: a systematic review of the current literature, a teleconference meeting, and then an initial survey were conducted based on the current evidence and expert opinion, and sent to the committee. Twenty-four experts in training, including clinicians, academics, and industry, contributed to the Delphi process. An accelerated Delphi process underwent three rounds and was completed within 72 h. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. Consensus opinion was defined as ≥80% agreement. EVIDENCE SYNTHESIS: There was 100% consensus regarding an urgent need for international agreement on guidance for optimised telepresence. Consensus was reached in multiple areas, including (1) infrastructure and functionality; (2) definitions and terminology; (3) protocols for training, communication, and safety issues; and (4) accountability including ethical and legal issues. The resulting formulated guidance showed good internal consistency among experts, with a Cronbach alpha of 0.90. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts for development and content validation of optimised telepresence services for robotic surgery training. This guidance lays the foundation for launching telepresence services in robotic surgery. This guidance will require further validation. PATIENT SUMMARY: Owing to travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic, development of remote training and support via telemedicine is becoming increasingly important. We report a key opinion leader consensus view on a standardised approach to telepresence.