Syndromic Surveillance for Estimating Hospital Demand During COVID-19 Pandemic

Conceptual of sickness woman feeling when admitted in the hospital.
The researchers’ goal was to determine if mandatory daily employee symptom attestation data could be used for syndromic surveillance to estimate COVID-19 hospitalizations in the communities where employees live.

The use of syndromic surveillance has demonstrated particular utility during the COVID-19 pandemic — a time when decision-making and health care resources need to be mobilized rapidly, when only imperfect, inconsistent information is available. A cohort study was conducted at a large academic hospital network in Massachusetts that comprises 10 hospitals, and the results of the analysis were published in the journal JAMA Network Open

The researchers sought to establish whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to estimate the rate of hospitalizations for COVID-19 in the communities in which the employees reside. The study was performed between April 2, 2020, and November 4, 2020.

In the large hospital system in which the survey was conducted, the 10 hospitals involved accounted for a total of 2384 staffed beds and 136,000 annual inpatient discharges during the designated time period. Of the 10 hospitals, hospital 1 is a tertiary academic teaching hospital located in Boston, Massachusetts, and has 719 staffed beds and 40,000 annual inpatient discharges.

Participants were included in the current analysis if they were employed at the urban tertiary care hospital and had been working on-site that day. All self-reported symptoms were recorded by the particular institution. Employees who lived outside the 10-hospital network were excluded from the study if they were not working on-site that day. Those employees who indicated that they would be working on-site that day were requested to fill out a symptom reporting form, on which they were asked whether they were experiencing any of 12 listed COVID-19 symptoms.

The primary study outcome was the mean absolute error (MAE) and weighted mean absolute percentage error (MAPE) of 7-day forecasts of daily COVID-19 hospitalizations across the 10-hospital network. The secondary outcome was the MAE and weighted MAPE of 7-day forecasts of weekly positive COVID-19 cases within each of the 10 hospital service areas in the hospital network.

Of the 6841 employees who lived within the 10-hospital service areas, 74.8% (5120 of 6841) of them were women and 56.8% (3884 of 6841) of them were White. The mean employee age was 40.8±13.6 years. The mean time of employee service was 8.8±10.4 years. The study model exhibited an MAE of 6.9 patients with COVID-19 and a weighted MAPE of 1.5% for hospitalizations throughout the entire hospital network.

The individual hospitals evaluated had an MAE of 0.9 to 4.5 patients (weighted MAPE, 2.1% to 16.1%). Because the mean network all-cause occupancy was 1286 during this time, an error of 6.9 represents only 0.5% of the network mean occupancy. At hospital 1, a doubling of the number of employees who reported COVID-19 symptoms was associated with a 5% increase in hospitalizations for COVID-19 in 7 days (95% CI, 0.02-0.07; P <.001).

The investigators concluded that using a real-time employee health attestation tool at a single hospital could help to estimate subsequent hospitalizations in 7 days at hospitals throughout a large hospital network in New England. This proved to be particularly useful at a time when more accurate data were not available, including during the current COVID-19 pandemic.


Horng S, O’Donoghue A, Dechen T, et al. Secondary use of COVID-19 symptom incidence among hospital employees as an example of syndromic surveillance of hospital admissions within 7 days. JAMA Netw Open. 2021;4(6):e2113782. doi:10.1001/jamanetworkopen.2021.13782