Evidence map›Paper›PMID 39510516›Full record

ArticleJMIR public health and surveillance2024

Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence.

Atushi Patel, Kevin Maruthananth, Neha Matharu, Andrew D Pinto, Banafshe Hosseini

Abstract readScoping Review
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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.

Atushi PatelUpstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.ORCID 0009-0004-0756-8149
Kevin MaruthananthUpstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.ORCID 0009-0006-7977-9572
Neha MatharuUpstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.ORCID 0009-0007-6944-9227
Andrew D PintoUpstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.ORCID 0000-0003-1841-9347
Banafshe HosseiniUpstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.ORCID 0000-0002-3962-2777

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection.

objectiveWe aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs.

methodsWe searched Ovid MEDLINE ALL, Embase, Cochrane Library (Wiley), and CINAHL (Ebsco). The search was conducted on October 03, 2023. Studies that implemented EWSs for the detection of acute respiratory illnesses were included. Covidence was used for citation management, and a modified Critical Appraisal Skills Programme (CASP) checklist was used for quality assessment.

resultsFrom 5838 initial articles, 29 met the inclusion criteria for this review. Twelve studies evaluated the use of EWSs within community settings, ranging from rural community reporting networks to urban online participatory surveillance platforms. Five studies focused on EWSs that used data from hospitalization and emergency department visits. These systems leveraged clinical and admission data to effectively detect and manage local outbreaks of respiratory infections. Two studies focused on the effectiveness of existing surveillance systems, assessing their adaptability and responsiveness to emerging threats and how they could be improved based on past performance. Four studies highlighted the integration of machine learning models to improve the predictive accuracy of EWSs. Three studies explored the applications of national EWSs in different health care settings and emphasized their potential in predicting clinical deterioration and facilitating early intervention. Lastly, 3 studies addressed the use of surveillance systems in aged-care facilities, highlighting the unique challenges and needs of monitoring and responding to health threats in environments housing vulnerable populations. The CASP tool revealed that most studies were relevant, reliable, and of high value (score 6: 11/29, 38%; score 5: 9/29, 31%). The common limitations included result generalizability, selection bias, and small sample size for model validation.

conclusionsThis scoping review confirms the critical role of EWSs in enhancing public health responses to respiratory infections. Although the effectiveness of these systems is evident, challenges related to generalizability and varying methodologies suggest a need for continued innovation and standardization in EWS development.

Indexed as

Respiratory Tract InfectionsAcute DiseaseEarly Warning ScoreGlobal HealthHumansacute respiratory infectionsearly detection systemsearly warning systems

Identifiers

PMID39510516
PMCPMC11582482

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.