Evidence map›Paper›PMID 36695448›Full record

ArticleEuro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin2023

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

Ivan Merlo, Mariano Crea, Paolo Berta, Francesca Ieva, Flavia Carle, Federico Rea, Gloria Porcu, Laura Savaré, Raul De Maio, Marco Villa and 11 more

Abstract read
In one paragraph

Article in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

21 authors.

Ivan MerloDepartment of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Mariano CreaItalian National Institute of Statistics, Rome, Italy.
Paolo BertaDepartment of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Francesca IevaMOX, Department of Mathematics, Politecnico di Milano, Milan, Italy.
Flavia CarleNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Federico ReaDepartment of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Gloria PorcuDepartment of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Laura SavaréMOX, Department of Mathematics, Politecnico di Milano, Milan, Italy.
Raul De MaioIconsulting S.p.a., Rome, Italy.
Marco VillaAgency for Health Protection of Val Padana, Lombardy Region, Cremona, Italy.
Danilo CeredaDirectorate General for Health, Lombardy Region, Milan, Italy.
Olivia LeoniDirectorate General for Health, Lombardy Region, Milan, Italy.
Francesco BortolanDirectorate General for Health, Lombardy Region, Milan, Italy.
Giuseppe Maria SechiAgenzia Regionale Emergenza Urgenza, Milan, Italy.
Antonino BellaItalian National Institute of Health (ISS), Rome, Italy.
Patrizio PezzottiItalian National Institute of Health (ISS), Rome, Italy.
Silvio BrusaferroItalian National Institute of Health (ISS), Rome, Italy.
Gian Carlo BlangiardoItalian National Institute of Statistics, Rome, Italy.
Massimo FedeliItalian National Institute of Statistics, Rome, Italy.
Giovanni CorraoDepartment of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
Italian Alert_CoV Project groupThe members of the project group have been listed at the end of this article.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

Indexed as

COVID-19Delivery of Health CareDisease OutbreaksHumansPandemicsPatient Acceptance of Health CareRetrospective StudiesCoronavirus disease (COVID-19)early outbreak detectionhealthcare utilisationhealthcare utilisation databasessmall areassyndromic surveillance

Identifiers

PMID36695448
PMCPMC9817206

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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.