Evidence map›Paper›PMID 34174852›Full record

ArticleBMC public health2021

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

Nicholas Papadomanolakis-Pakis, Allison Maier, Adam van Dijk, Nancy VanStone, Kieran Michael Moore

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Nicholas Papadomanolakis-PakisKnowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada. n.pakis@queensu.ca.
Allison MaierKnowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada.
Adam van DijkKnowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada.
Nancy VanStoneKnowledge Management Division, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada.
Kieran Michael MooreOffice of the Medical Officer of Health, Kingston, Frontenac and Lennox & Addington Public Health, 221 Portsmouth Avenue, Kingston, Ontario, K7M 1V5, Canada.
Kingston Health Sciences Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsBetween 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.

conclusionsOur 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.

Indexed as

COVID-19PandemicsHospitalsHumansOntarioSARS-CoV-2Sentinel SurveillanceCOVID-19Hospital admissionsHospitalizationsPandemicPublic health surveillanceSituational awarenessSurveillance systemSyndromic surveillance

Identifiers

PMID34174852
PMCPMC8233625
OpenAlexW3174361409

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.