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Surveillance Open Access
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Abstract

Background

Effective pandemic preparedness requires robust severe acute respiratory infection (SARI) surveillance. However, identifying SARI patients based on symptoms is time-consuming. Using the number of reverse transcription (RT)-PCR tests or contact and droplet precaution labels as a proxy for SARI could accurately reflect the epidemiology of patients presenting with SARI.

Aim

We aimed to compare the number of RT-PCR tests, contact and droplet precaution labels and SARI-related International Classification of Disease (ICD)-10 codes and evaluate their use as surveillance indicators.

Methods

Patients from all age groups hospitalised at Leiden University Medical Center between 1 January 2017 up to and including 30 April 2023 were eligible for inclusion. We used a clinical data collection tool to extract data from electronic medical records. For each surveillance indicator, we plotted the absolute count for each week, the incidence proportion per week and the correlation between the three surveillance indicators.

Results

We included 117,404 hospital admissions. The three surveillance indicators generally followed a similar pattern before and during the COVID-19 pandemic. The correlation was highest between contact and droplet precaution labels and ICD-10 diagnostic codes (Pearson correlation coefficient: 0.84). There was a strong increase in the number of RT-PCR tests after the start of the COVID-19 pandemic.

Discussion

All three surveillance indicators have advantages and disadvantages. ICD-10 diagnostic codes are suitable but are subject to reporting delays. Contact and droplet precaution labels are a feasible option for automated SARI surveillance, since these reflect trends in SARI incidence and may be available real-time.

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2024-07-04
2024-07-22
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2024.29.27.2300657
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References

  1. Marbus SD, van der Hoek W, van Dissel JT, van Gageldonk-Lafeber AB. Experience of establishing severe acute respiratory surveillance in the Netherlands: Evaluation and challenges. Public Health Pract (Oxf). 2020;1:100014.  https://doi.org/10.1016/j.puhip.2020.100014  PMID: 34171043 
  2. Marbus SD, Oost JA, van der Hoek W, Meijer A, Polderman FN, de Jager CPC, et al. Ernstige acute luchtweginfecties: De ontbrekende bouwsteen in de surveillancepiramide. [Severe acute respiratory infections: the missing building block in the surveillance pyramid.] Ned Tijdschr Med Micrbiol. 2016;24(1):52-6. Dutch. Available from: https://www.rivm.nl/publicaties/ernstige-acute-luchtweginfecties-ontbrekende-bouwsteen-in-surveillancepiramide
  3. European Centre for Disease Prevention and Control (ECDC). Acute respiratory infections in the EU/EEA: Epidemiological update and current public health recommendations. Stockholm: ECDC; 2023. Available from: https://www.ecdc.europa.eu/en/news-events/acute-respiratory-infections-eueea-epidemiological-update-and-current-public-health
  4. Chiolero A, Buckeridge D. Glossary for public health surveillance in the age of data science. J Epidemiol Community Health. 2020;74(7):612-6.  https://doi.org/10.1136/jech-2018-211654  PMID: 32332114 
  5. Buda S, Tolksdorf K, Schuler E, Kuhlen R, Haas W. Establishing an ICD-10 code based SARI-surveillance in Germany - description of the system and first results from five recent influenza seasons. BMC Public Health. 2017;17(1):612.  https://doi.org/10.1186/s12889-017-4515-1  PMID: 28666433 
  6. Torres AR, Gómez V, Kislaya I, Rodrigues AP, Fernandes Tavares M, Pereira AC, et al. Monitoring COVID-19 and influenza: the added value of a severe acute respiratory infection surveillance system in Portugal. Can J Infect Dis Med Microbiol. 2023;2023:6590011.  https://doi.org/10.1155/2023/6590011  PMID: 36846348 
  7. Wells J, Young JJ, Harvey C, Mutch H, McPhail D, Young N, et al. Real-time surveillance of severe acute respiratory infections in Scottish hospitals: an electronic register-based approach, 2017-2022. Public Health. 2022;213:5-11.  https://doi.org/10.1016/j.puhe.2022.09.003  PMID: 36306639 
  8. World Health Organization (WHO). International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10 Version:2019). Geneva: WHO; 2019. Available from: https://icd.who.int/browse10/2019/en
  9. World Health Organization (WHO). Prevention, identification and management of health worker infection in the context of COVID-19. Geneva: WHO; 2020. Available from: https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-HW_infection-2020.1
  10. de Koning ER, Boogers MJ, Beeres SL, Kramer ID, Dannenberg WJ, Schalij MJ. Managing Hospital Capacity: Achievements and Lessons from the COVID-19 Pandemic. Prehosp Disaster Med. 2022;37(5):600-8.  https://doi.org/10.1017/S1049023X22001169  PMID: 35950299 
  11. Abedian Kalkhoran H, Zwaveling J, Storm BN, van Laar SA, Portielje JE, Codrington H, et al. A text-mining approach to study the real-world effectiveness and potentially fatal immune-related adverse events of PD-1 and PD-L1 inhibitors in older patients with stage III/IV non-small cell lung cancer. BMC Cancer. 2023;23(1):247.  https://doi.org/10.1186/s12885-023-10701-z  PMID: 36918817 
  12. van Laar SA, Gombert-Handoko KB, Guchelaar HJ, Zwaveling J. An electronic health record text mining tool to collect real-world drug treatment outcomes: a validation study in patients with metastatic renal cell carcinoma. Clin Pharmacol Ther. 2020;108(3):644-52.  https://doi.org/10.1002/cpt.1966  PMID: 32575147 
  13. Alderweireld CEA, Buiting AGM, Murk JAN, Verweij JJ, Berrevoets MAH, van Kasteren MEE. COVID-19: patiënt nul in Nederland. [COVID-19: patient zero in the Netherlands]. Ned Tijdschr Geneeskd. 2020;164:D4962. Dutch. PMID: 32613784 
  14. Klous G, McDonald S, de Boer P, van Hoek A, Franz E. Staat van infectieziekten in nederland, 2021. [State of Infectious Diseases in the Netherlands, 2021]. Bilthoven: Rijksinstituut voor Volksgezondheid en Milieu(RIVM); 2021. Available from: https://doi.org/10.21945/RIVM-2022-0141
  15. Dongelmans DA, Termorshuizen F, Brinkman S, Bakhshi-Raiez F, Arbous MS, de Lange DW, et al. Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands. Ann Intensive Care. 2022;12(1):5.  https://doi.org/10.1186/s13613-021-00978-3  PMID: 35024981 
  16. European Centre for Disease Prevention and Control (ECDC). Severe acute respiratory infections (SARI) reporting protocol version 3.8. Stockholm: ECDC; 2023. Available from: https://www.ecdc.europa.eu/en/publications-data/severe-acute-respiratory-infections-sari-reporting-protocol
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