学术文献库

Importance: Alternative methods for hospital utilization forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited. Objective: Determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to forecast COVID-19 hospitalizations in the communities where employees live. Design: Retrospective cohort study. Setting: Large academic hospital network of 10 hospitals accounting for a total of 2,384 beds and 136,000 discharges in New England. Participants: 6,841 employees working on-site of Hospital 1 from April 2, 2020 to November 4, 2020, who live in the 10 hospitals' service areas. Interventions: Mandatory, daily employee self-reported symptoms were collected using an automated text messaging system. Main Outcomes: Mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) of 7 day forecasts of daily COVID-19 hospital census at each hospital. Results: 6,841 employees, with a mean age of 40.8 (SD = 13.6), 8.8 years of service (SD = 10.4), and 74.8% were female (n = 5,120), living in the 10 hospitals' service areas. Our model has an MAE of 6.9 COVID-19 patients and a WMAPE of 1.5% for hospitalizations for the entire hospital network. The individual hospitals had an MAE that ranged from 0.9 to 4.5 patients (WMAPE ranged from 2.1% to 16.1%). At Hospital 1, a doubling of the number of employees reporting symptoms (which corresponds to 4 additional employees reporting symptoms at the mean for Hospital 1) is associated with a 5% increase in COVID-19 hospitalizations at Hospital 1 in 7 days (95% CI: (0.02, 0.07)). Conclusions: We found that a real-time employee health attestation tool used at a single hospital could be used to predict subsequent hospitalizations in 7 days at hospitals throughout a larger hospital network in New England.