Secondary Use of COVID-19 Symptom Incidence Among Hospital Employees as an Example of Syndromic Surveillance of Hospital Admissions Within 7 Days

Abstract

IMPORTANCE Alternative methods for hospital occupancy forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited.OBJECTIVE To determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to estimate COVID-19 hospitalizations in the communities where employees live. DESIGN, SETTING, AND PARTICIPANTSThis cohort study was conducted from April 2, 2020, to November 4, 2020, at a large academic hospital network of 10 hospitals accounting for a total of 2384 beds and 136 000 discharges in New England. The participants included 6841 employees who worked on-site at hospital 1 and lived in the 10 hospitals' service areas.EXPOSURE Daily employee self-reported symptoms were collected using an automated text messaging system from a single hospital. MAIN OUTCOMES AND MEASURESMean absolute error (MAE) and weighted mean absolute percentage error (MAPE) of 7-day forecasts of daily COVID-19 hospital census at each hospital. RESULTS Among 6841 employees living within the 10 hospitals' service areas, 5120 (74.8%) were female individuals and 3884 (56.8%) were White individuals; the mean (SD) age was 40.8 (13.6) years, and the mean (SD) time of service was 8.8 (10.4) years. The study model had a MAE of 6.9 patients with COVID-19 and a weighted MAPE 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 (weighted MAPE ranged from 2.1% to 16.1%). For context, the mean network all-cause occupancy was 1286 during this period, so an error of 6.9 is only 0.5% of the network mean occupancy. Operationally, this level of error was negligible to the incident command center. At hospital 1, a doubling of the number of employees reporting symptoms (which corresponded to 4 additional employees reporting symptoms at the mean for hospital 1) was associated with a 5% increase in COVID-19 hospitalizations at hospital 1 in 7 days (regression coefficient, 0.05; 95% CI, 0.02-0.07; P < .001).CONCLUSIONS AND RELEVANCE This cohort study found that a real-time employee health attestation tool used at a single hospital could be used to estimate subsequent hospitalizations in 7 days at hospitals throughout a larger hospital network in New England.

Coronavirus disease 2019 (COVID-19) screening system utilizing daily symptom attestation helps identify hospital employees who should be tested to protect patients and coworkers

“…The characteristics of employees who submitted at least 1 attestation are similar to the employee demographics of another academic medical center in the same part of the country as reported by Horng et al 14 Notably, during the reported months, employees only attested when physically entering a hospital or clinic. An employee may have worked more days than the number of attestations they submitted, for instance, if they transitioned to working remotely.…”

“…Per employee, the median number of attestations was 33 (range, 1-99); the median number of COVID-19 tests was 0 (range, 0-13), and the median number of positive tests was 0 (range, 0-10). The number of languages used for attestations was 1 (range, 1-4), and the number of hospital and/or clinic locations was 1 (range, [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

COVID-19 Infection, Prevention, and Control Considerations in the Obstetric Environment

“…While self-administered screening and attestation have limitations as they rely on accurate reporting by HCP, accessibility to a mobile device, and increased staffing at facility entrances, these strategies have been widely implemented and have identified symptomatic HCP. 6,7 As such, they add to the armamentarium of strategies to minimize COVID-19 transmission in health care facilities. Health care facilities should develop policies and procedures to advise HCP who screen positive about testing for SARS-CoV-2 and when it is safe to return to work.…”

A composite clinical motor score as a comprehensive and sensitive outcome measure for Parkinson’s disease

“…An exhaustive and detailed validation was undertaken to gauge the efficacy of the proposed composite clinical motor score in providing a comprehensive overview of motor symptoms, by comparing it with the MDS-UPDRS III based on the following seven criteria: (1) discrimination accuracy : the ability to distinguish between the disease groups (controls, iRBD and PD), whereby the accuracy was quantified using AUC values; (2) consistency : using the coefficient of variation (SD/mean) as an indicator of overall variability relative to the mean33 34 and using the weighted mean absolute percentage change (for control participants who underwent baseline and repeat in-person assessments: defined as the sum of each participant’s absolute change in score between baseline and repeat assessments/the sum of all baseline scores, with the value expressed as a percentage), a measure akin to the weighted mean absolute percentage error (wMAPE),35 which allows the quantification of forecasting errors relative to a ground truth, bypassing errors that would arise with baseline values of 0, were the mean absolute percentage change to be calculated; (3) correlation : measuring Spearman’s correlation with other clinical measures; (4) sensitivity to disease stage : change in the score values for different stages of disease severity (as assessed via the H&Y stage); (5) longitudinal progression : ability to track disease progression over time, for all participants with iRBD and PD—additionally, for participants with PD, the longitudinal standardised score trajectories according to baseline cluster (PD cluster 1: fast motor progression with symmetrical motor disease, poor olfaction, cognition and postural hypotension; PD cluster 2: mild motor and non-motor disease with intermediate motor progression; PD cluster 3: severe motor disease, poor psychological well-being and poor sleep with an intermediate motor progression; PD cluster 4: slow motor progression with tremor-dominant, unilateral disease), as previously described, were also compared to determine whether the trajectories were preserved across scores36; (6) relative linearity : as calculated by the wMAPE (the sum of the absolute difference between the true and predicted scores, divided by the sum of the true scores) of individualised linear predictions based on two or more composite clinical motor scores from the same individual at discrete time points being used to predict their composite clinical motor scores at other time points; and (7) prediction accuracy : ability to predict clinical outcomes, quantified using AUC values, in PD including (a) falls (at least one self-reported fall in the preceding 6 months), (b) freezing (a frequency of freezing other than ‘never’; ie, a score of at least 1 in answer to the question ‘Do you feel that your feet get glued to the floor while walking, making a turn or when trying to initiate walking (freezing)?’ on the Freezing of Gait Questionnaire), (c) cognitive impairment (a score of 1 or more on MDS-UPDRS I item 1.1), and (d) problems with self-care and performing usual activities (a score of >1 on each EQ-5D-3L item, denoting the presence of at least some problems).…”