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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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This work is licensed under a Creative Commons Attribution 4.0 International License.
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/content/10.2807/1560-7917.ES.2024.29.27.2300657
2024-07-04
2024-12-21
/content/10.2807/1560-7917.ES.2024.29.27.2300657
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Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Maaike+C+Swets&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Swets Maaike C</a>, <a href="/search?value1=Annabel+Niessen&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Niessen Annabel</a>, <a href="/search?value1=Emilie+P+Buddingh&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Buddingh Emilie P</a>, <a href="/search?value1=Ann+CTM+Vossen&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Vossen Ann CTM</a>, <a href="/search?value1=Karin+Ellen+Veldkamp&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Veldkamp Karin Ellen</a>, <a href="/search?value1=Irene+K+Veldhuijzen&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Veldhuijzen Irene K</a>, <a href="/search?value1=Mark+GJ+de+Boer&option1=author&noRedirect=true" class="nonDisambigAuthorLink">de Boer Mark GJ</a>, <a href="/search?value1=Geert+H+Groeneveld&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Groeneveld Geert H</a></span>. Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study. <a href="/content/ecdc">Euro Surveill.</a> 2024;29(27):pii=2300657. <a href="https://doi.org/10.2807/1560-7917.ES.2024.29.27.2300657" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2024.29.27.2300657</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 21 Nov 2023;&nbsp;&nbsp; <span class="generated">Accepted</span>: 26 Mar 2024 </span> </p>
/content/10.2807/1560-7917.ES.2024.29.27.2300657
/content/10.2807/1560-7917.ES.2024.29.27.2300657
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