RESUMEN
On May 21, 2020, the Harvard Program in Global Surgery and Social Change (PGSSC) hosted a webinar as part of the Harvard Medical School Department of Global Health and Social Medicine's COVID-19 webinar series. The goal of PGSSC's virtual webinar was to share the experiences of surgical, anesthesia, and obstetric (SAO) providers on the frontlines of the COVID pandemic, from both high-income countries (HICs), such as the United States and the United Kingdom, as well as low- and middle-income countries (LMICs). Providers shared not only their experiences delivering SAO care during this global pandemic, but also solutions and innovations they and their colleagues developed to address these new challenges. Additionally, the seminar explored the relationship between surgery and health system strengthening and pandemic preparedness, and outlined the way forward, including a roadmap for prioritization and investment in surgical system strengthening. Throughout the discussion, other themes emerged as well, such as the definition of elective surgery and its implications during a persistent global pandemic, the safe and ethical reintroduction of surgical services, and the social inequities exposed by the stress placed on health systems by COVID-19. These proceedings document the perspectives shared by participants through their invited lectures as well as through the panel discussion at the end of the seminar.
RESUMEN
OBJECTIVES: To assist with planning hospital resources, including critical care (CC) beds, for managing patients with COVID-19. METHODS: An individual simulation was implemented in Microsoft Excel using a discretely integrated condition event simulation. Expected daily cases presented to the emergency department were modeled in terms of transitions to and from ward and CC and to discharge or death. The duration of stay in each location was selected from trajectory-specific distributions. Daily ward and CC bed occupancy and the number of discharges according to care needs were forecast for the period of interest. Face validity was ascertained by local experts and, for the case study, by comparing forecasts with actual data. RESULTS: To illustrate the use of the model, a case study was developed for Guy's and St Thomas' Trust. They provided inputs for January 2020 to early April 2020, and local observed case numbers were fit to provide estimates of emergency department arrivals. A peak demand of 467 ward and 135 CC beds was forecast, with diminishing numbers through July. The model tended to predict higher occupancy in Level 1 than what was eventually observed, but the timing of peaks was quite close, especially for CC, where the model predicted at least 120 beds would be occupied from April 9, 2020, to April 17, 2020, compared with April 7, 2020, to April 19, 2020, in reality. The care needs on discharge varied greatly from day to day. CONCLUSIONS: The DICE simulation of hospital trajectories of patients with COVID-19 provides forecasts of resources needed with only a few local inputs. This should help planners understand their expected resource needs.