Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

Abstract

Background: The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

Methods: We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

Results: Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

Conclusions: Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Keywords: COVID-19; Hospital admissions; Hospitalizations; Pandemic; Public health surveillance; Situational awareness; Surveillance system; Syndromic surveillance.

Fig. 1

Trends in 7-day averages among suspected COVID-19 hospital admissions and confirmed COVID-19 cases between March 1, 2020 and September 21, 2020 in Ontario, Canada. ILI Influenza-like illness, INF General infection, PN Pneumonia, SOI Syndromes of interest. Note: Total confirmed cases (dotted-line) refers to the y-axis on the right

Fig. 2

Time series lag analysis of suspected COVID-19 admissions compared with confirmed COVID-19 admissions in Ontario. The blue dotted lines represent 95% confidence intervals (CI) for lack of trend (0.229). Values that exceed these lines represent time-lagged trends. A CCF greater than the CI indicates a probable connection between the timing of suspected COVID-19 (excluding SOI) admissions and confirmed COVID-19 admissions. A lag of − 3 indicates that confirmed COVID-19 admissions lagged suspected COVID-19 admissions by 3 days. CCF values slightly greater than the CI at − 7 and 7 days are likely due to regular weekly fluctuations in hospital usage and acute/care patterns