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Merlo I, Crea M, Berta P, Ieva F, Carle F, Rea F, Porcu G, Savaré L, De Maio R, Villa M, Cereda D, Leoni O, Bortolan F, Sechi GM, Bella A, Pezzotti P, Brusaferro S, Blangiardo GC, Fedeli M, Corrao G. 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. Euro Surveill 2023; 28:2200366. [PMID: 36695448 PMCID: PMC9817206 DOI: 10.2807/1560-7917.es.2023.28.1.2200366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/02/2022] [Indexed: 01/07/2023] Open
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.
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Affiliation(s)
- Ivan Merlo
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Mariano Crea
- Italian National Institute of Statistics, Rome, Italy
| | - Paolo Berta
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Francesca Ieva
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center for Health Data Science, Human Technopole, Milan, Italy
| | - Flavia Carle
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy
| | - Federico Rea
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
| | - Gloria Porcu
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
| | - Laura Savaré
- MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco Villa
- Agency for Health Protection of Val Padana, Lombardy Region, Cremona, Italy
| | - Danilo Cereda
- Directorate General for Health, Lombardy Region, Milan, Italy
| | - Olivia Leoni
- Directorate General for Health, Lombardy Region, Milan, Italy
| | | | | | | | | | | | | | | | - Giovanni Corrao
- Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy
- National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
- Directorate General for Health, Lombardy Region, Milan, Italy
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Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling BN. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health 2022; 22:2216. [PMCID: PMC9707072 DOI: 10.1186/s12889-022-14625-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Abstract
Background
Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings.
Methods
We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist.
Results
Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments’ data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians’ reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches.
Conclusions
Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs’ method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.
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