1887
  1. Home
  2. Eurosurveillance
  3. Volume 28, Issue 1, 05/Jan/2023
  4. Article
Research Open Access
Like 0
Download

Abstract

Background

During the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.

Aim

To develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.

Methods

Data 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.

Results

We 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.

Conclusion

Implementing 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.

© Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Loading

Article metrics loading...

/content/10.2807/1560-7917.ES.2023.28.1.2200366
2023-01-05
2024-11-22
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2023.28.1.2200366
Loading
Loading full text...

Full text loading...

/deliver/fulltext/eurosurveillance/28/1/eurosurv-28-1-5.html?itemId=/content/10.2807/1560-7917.ES.2023.28.1.2200366&mimeType=html&fmt=ahah

References

  1. Thacker SB, Choi K, Brachman PS. The surveillance of infectious diseases. JAMA. 1983;249(9):1181-5.  https://doi.org/10.1001/jama.1983.03330330059036  PMID: 6823080 
  2. Hashimoto S, Murakami Y, Taniguchi K, Nagai M. Detection of epidemics in their early stage through infectious disease surveillance. Int J Epidemiol. 2000;29(5):905-10.  https://doi.org/10.1093/ije/29.5.905  PMID: 11034976 
  3. Meyerowitz EA, Richterman A, Gandhi RT, Sax PE. Transmission of SARS-CoV-2: A review of viral, host, and environmental factors. Ann Intern Med. 2021;174(1):69-79.  https://doi.org/10.7326/M20-5008  PMID: 32941052 
  4. Kubota Y, Shiono T, Kusumoto B, Fujinuma J. Multiple drivers of the COVID-19 spread: The roles of climate, international mobility, and region-specific conditions. PLoS One. 2020;15(9):e0239385.  https://doi.org/10.1371/journal.pone.0239385  PMID: 32966315 
  5. Nicodemo C, Barzin S, Cavalli N, Lasserson D, Moscone F, Redding S, et al. Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability. BMJ Open. 2020;10(9):e039749.  https://doi.org/10.1136/bmjopen-2020-039749  PMID: 32994257 
  6. Lewis D. Why many countries failed at COVID contact-tracing - but some got it right. Nature. 2020;588(7838):384-7.  https://doi.org/10.1038/d41586-020-03518-4  PMID: 33318682 
  7. Shmueli G, Burkom H. Statistical challenges facing early outbreak detection in biosurveillance. Technometrics. 2010;52(1):39-51.  https://doi.org/10.1198/TECH.2010.06134 
  8. World Health Organization (WHO). Early detection, assessment and response to acute public health events: Implementation of early warning and response with a focus on event-based surveillance: Interim version. Geneva: WHO; 2014‎. Available from: https://apps.who.int/iris/bitstream/handle/10665/112667/WHO_HSE_GCR_LYO_2014.4_eng.pdf?sequence=1&isAllowed=y
  9. Li C, Chen LJ, Chen X, Zhang M, Pang CP, Chen H. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Euro Surveill. 2020;25(10):2000199.  https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000199  PMID: 32183935 
  10. Venkatesh U, Gandhi PA. Prediction of COVID-19 outbreaks using Google trends in India: A retrospective analysis. Healthc Inform Res. 2020;26(3):175-84.  https://doi.org/10.4258/hir.2020.26.3.175  PMID: 32819035 
  11. Shah S, Gwee SXW, Ng JQX, Lau N, Koh J, Pang J. Wastewater surveillance to infer COVID-19 transmission: A systematic review. Sci Total Environ. 2022;804:150060.  https://doi.org/10.1016/j.scitotenv.2021.150060  PMID: 34798721 
  12. Paterson BJ, Durrheim DN. The remarkable adaptability of syndromic surveillance to meet public health needs. J Epidemiol Glob Health. 2013;3(1):41-7.  https://doi.org/10.1016/j.jegh.2012.12.005  PMID: 23856537 
  13. Hulth A. First European guidelines on syndromic surveillance in human and animal health published. Euro Surveill. 2014;19(41):20927.  https://doi.org/10.2807/ese.19.41.20927-en  PMID: 25345523 
  14. European Centre for Disease Prevention and Control (ECDC). Assessment of electronic health records for infectious disease surveillance: Final mapping exercise report. Stockholm: ECDC; 2021
  15. Mehl A, Bergey F, Cawley C, Gilsdorf A. Syndromic surveillance insights from a symptom assessment app before and during COVID-19 measures in Germany and the United Kingdom: Results from repeated cross-sectional analyses. JMIR Mhealth Uhealth. 2020;8(10):e21364.  https://doi.org/10.2196/21364  PMID: 32997640 
  16. Kennedy B, Fitipaldi H, Hammar U, Maziarz M, Tsereteli N, Oskolkov N, et al. App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden. Nat Commun. 2022;13(1):2110.  https://doi.org/10.1038/s41467-022-29608-7  PMID: 35449172 
  17. Winkelman TNA, Margolis KL, Waring S, Bodurtha PJ, Khazanchi R, Gildemeister S, et al. Minnesota Electronic Health Record Consortium COVID-19 Project: Informing pandemic response through statewide collaboration using observational data. Public Health Rep. 2022;137(2):263-71.  https://doi.org/10.1177/00333549211061317  PMID: 35060411 
  18. Yom-Tov E. Active syndromic surveillance of COVID-19 in Israel. Sci Rep. 2021;11(1):24449.  https://doi.org/10.1038/s41598-021-03977-3  PMID: 34961786 
  19. Lazarus R, Kleinman KP, Dashevsky I, DeMaria A, Platt R. Using automated medical records for rapid identification of illness syndromes (syndromic surveillance): the example of lower respiratory infection. BMC Public Health. 2001;1(1):9.  https://doi.org/10.1186/1471-2458-1-9  PMID: 11722798 
  20. de Fougerolles TR, Damm O, Ansaldi F, Chironna M, Crépey P, de Lusignan S, et al. National influenza surveillance systems in five European countries: a qualitative comparative framework based on WHO guidance. BMC Public Health. 2022;22(1):1151.  https://doi.org/10.1186/s12889-022-13433-0  PMID: 35681199 
  21. Corrao G, Mancia G. Generating evidence from computerized healthcare utilization databases. Hypertension. 2015;65(3):490-8.  https://doi.org/10.1161/HYPERTENSIONAHA.114.04858  PMID: 25624339 
  22. Hughes HE, Edeghere O, O’Brien SJ, Vivancos R, Elliot AJ. Emergency department syndromic surveillance systems: a systematic review. BMC Public Health. 2020;20(1):1891.  https://doi.org/10.1186/s12889-020-09949-y  PMID: 33298000 
  23. Hiller KM, Stoneking L, Min A, Rhodes SM. Syndromic surveillance for influenza in the emergency department-A systematic review. PLoS One. 2013;8(9):e73832.  https://doi.org/10.1371/journal.pone.0073832  PMID: 24058494 
  24. Ageron FX, Hugli O, Dami F, Caillet-Bois D, Pittet V, Eckert P, et al. Lessons from COVID-19 syndromic surveillance through emergency department activity: a prospective time series study from western Switzerland. BMJ Open. 2022;12(5):e054504.  https://doi.org/10.1136/bmjopen-2021-054504  PMID: 35523491 
  25. Lukowsky LR, Der-Martirosian C, Steers WN, Kamble KS, Dobalian A. Using an administrative and clinical database to determine the early spread of COVID-19 at the US Department of Veterans Affairs during the beginning of the 2019-2020 flu season: A retrospective longitudinal study. Viruses. 2022;14(2):200.  https://doi.org/10.3390/v14020200  PMID: 35215795 
  26. Skrami E, Carle F, Villani S, Borrelli P, Zambon A, Corrao G, et al. Availability of real-world data in Italy: A tool to navigate regional healthcare utilization databases. Int J Environ Res Public Health. 2019;17(1):8.  https://doi.org/10.3390/ijerph17010008  PMID: 31861310 
  27. Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An improved algorithm for outbreak detection in multiple surveillance systems. Stat Med. 2013;32(7):1206-22.  https://doi.org/10.1002/sim.5595  PMID: 22941770 
  28. Höhle M, Paul M. Count data regression charts for the monitoring of surveillance time series. Comput Stat Data Anal. 2008;52(9):4357-68.  https://doi.org/10.1016/j.csda.2008.02.015 
  29. Salmon M, Schumacher D, Höhle M. Monitoring count time series in R: Aberration detection in public health surveillance. J Stat Softw. 2016;70(10):1-35.  https://doi.org/10.18637/jss.v070.i10 
  30. Vacek PM. The effect of conditional dependence on the evaluation of diagnostic tests. Biometrics. 1985;41(4):959-68.  https://doi.org/10.2307/2530967  PMID: 3830260 
  31. Cressie N. Statistics for spatial data. New York: Wiley; 1993.
  32. Ramsay J, Silverman B. Functional data analysis (2nd ed). New York: Springer; 2005.
  33. Coloma PM, Schuemie MJ, Trifirò G, Gini R, Herings R, Hippisley-Cox J, et al. , EU-ADR Consortium. Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project. Pharmacoepidemiol Drug Saf. 2011;20(1):1-11.  https://doi.org/10.1002/pds.2053  PMID: 21182150 
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
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Ivan+Merlo&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Merlo Ivan</a>, <a href="/search?value1=Mariano+Crea&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Crea Mariano</a>, <a href="/search?value1=Paolo+Berta&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Berta Paolo</a>, <a href="/search?value1=Francesca+Ieva&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Ieva Francesca</a>, <a href="/search?value1=Flavia+Carle&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Carle Flavia</a>, <a href="/search?value1=Federico+Rea&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Rea Federico</a>, <a href="/search?value1=Gloria+Porcu&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Porcu Gloria</a>, <a href="/search?value1=Laura+Savar%C3%A9&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Savaré Laura</a>, <a href="/search?value1=Raul+De+Maio&option1=author&noRedirect=true" class="nonDisambigAuthorLink">De Maio Raul</a>, <a href="/search?value1=Marco+Villa&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Villa Marco</a>, <a href="/search?value1=Danilo+Cereda&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Cereda Danilo</a>, <a href="/search?value1=Olivia+Leoni&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Leoni Olivia</a>, <a href="/search?value1=Francesco+Bortolan&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Bortolan Francesco</a>, <a href="/search?value1=Giuseppe+Maria+Sechi&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Sechi Giuseppe Maria</a>, <a href="/search?value1=Antonino+Bella&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Bella Antonino</a>, <a href="/search?value1=Patrizio+Pezzotti&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Pezzotti Patrizio</a>, <a href="/search?value1=Silvio+Brusaferro&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Brusaferro Silvio</a>, <a href="/search?value1=Gian+Carlo+Blangiardo&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Blangiardo Gian Carlo</a>, <a href="/search?value1=Massimo+Fedeli&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Fedeli Massimo</a>, <a href="/search?value1=Giovanni+Corrao&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Corrao Giovanni</a>, <a href="/search?value1=on+behalf+of+the+Italian+Alert_CoV+Project+group&option1=author&noRedirect=true" class="nonDisambigAuthorLink">on behalf of the Italian Alert_CoV Project group</a></span>. 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. <a href="/content/ecdc">Euro Surveill.</a> 2023;28(1):pii=2200366. <a href="https://doi.org/10.2807/1560-7917.ES.2023.28.1.2200366" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2023.28.1.2200366</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 22 Apr 2022;&nbsp;&nbsp; <span class="generated">Accepted</span>: 02 Oct 2022 </span> </p>
/content/10.2807/1560-7917.ES.2023.28.1.2200366
/content/10.2807/1560-7917.ES.2023.28.1.2200366
Loading

Data & Media loading...

Supplementary data

Submit comment
Close
Comment moderation successfully completed
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error