Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires.
<h4>Background</h4>Some of those infected with SARS-CoV-2 suffer from post-COVID syndrome (PCS). However, an uniform definition of PCS is lacking, causing uncertainty about the prevalence and nature of this syndrome. We aimed to improve understanding of PCS by operationalizing different...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318272 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206768586719232 |
|---|---|
| author | Isabelle Bos Lisa Bosman Rinske van den Hoek Willemijn van Waarden Matthijs S Berends Maarten S Homburg Tim Olde Hartman Jean Muris Lilian S Peters Bart Knottnerus Karin S Hek Robert A Verheij |
| author_facet | Isabelle Bos Lisa Bosman Rinske van den Hoek Willemijn van Waarden Matthijs S Berends Maarten S Homburg Tim Olde Hartman Jean Muris Lilian S Peters Bart Knottnerus Karin S Hek Robert A Verheij |
| author_sort | Isabelle Bos |
| collection | DOAJ |
| description | <h4>Background</h4>Some of those infected with SARS-CoV-2 suffer from post-COVID syndrome (PCS). However, an uniform definition of PCS is lacking, causing uncertainty about the prevalence and nature of this syndrome. We aimed to improve understanding of PCS by operationalizing different classifications and to explore clinical subtypes.<h4>Methods</h4>We used data from Nivel Primary Care database from 2019-2020 which consists of electronic health records (EHR) from general practices (GPs) combined with sociodemographic data for n = 10,313 individuals infected with the SARS-CoV-2. In addition, data from n = 276 individuals who had been infected with the SARS-CoV-2 in 2021, collected via a longitudinal survey, was used. In the GP-EHR data, we operationalized two classifications of PCS (based on symptoms and diagnosis recorded in GP-EHR data and healthcare utilization 3-12 months after acute infection) to calculate frequency and characteristics and compared this to the survey results. In a subgroup of the EHR data we conducted community detection analyses to explore clinical subtypes of PCS.<h4>Results</h4>The frequency of PCS was 15% with on average 4.6 symptoms for which the GP was consulted using the narrow definition and 32% with on average 6.8 symptoms for the broad definition. Across all methods and classifications, the mean age of individuals with PCS was around 53 years and they were more often female. There were small sex differences in the type of symptoms and overall symptoms were persistent for 6 months. The community detection analysis revealed three possible clinical subtypes.<h4>Discussion</h4>We showed that frequency rates of PCS differ between methods and data sources, but characteristics of the affected individuals are relatively stable. Overall, PCS is a heterogeneous syndrome affecting a substantial group of individuals who need adequate care. Future studies should focus on care trajectories and qualitative measures such as quality of life of individuals living with PCS. |
| format | Article |
| id | doaj-art-12ec856d8e354892a49bdbaad443fc23 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-12ec856d8e354892a49bdbaad443fc232025-02-07T05:30:53ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031827210.1371/journal.pone.0318272Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires.Isabelle BosLisa BosmanRinske van den HoekWillemijn van WaardenMatthijs S BerendsMaarten S HomburgTim Olde HartmanJean MurisLilian S PetersBart KnottnerusKarin S HekRobert A Verheij<h4>Background</h4>Some of those infected with SARS-CoV-2 suffer from post-COVID syndrome (PCS). However, an uniform definition of PCS is lacking, causing uncertainty about the prevalence and nature of this syndrome. We aimed to improve understanding of PCS by operationalizing different classifications and to explore clinical subtypes.<h4>Methods</h4>We used data from Nivel Primary Care database from 2019-2020 which consists of electronic health records (EHR) from general practices (GPs) combined with sociodemographic data for n = 10,313 individuals infected with the SARS-CoV-2. In addition, data from n = 276 individuals who had been infected with the SARS-CoV-2 in 2021, collected via a longitudinal survey, was used. In the GP-EHR data, we operationalized two classifications of PCS (based on symptoms and diagnosis recorded in GP-EHR data and healthcare utilization 3-12 months after acute infection) to calculate frequency and characteristics and compared this to the survey results. In a subgroup of the EHR data we conducted community detection analyses to explore clinical subtypes of PCS.<h4>Results</h4>The frequency of PCS was 15% with on average 4.6 symptoms for which the GP was consulted using the narrow definition and 32% with on average 6.8 symptoms for the broad definition. Across all methods and classifications, the mean age of individuals with PCS was around 53 years and they were more often female. There were small sex differences in the type of symptoms and overall symptoms were persistent for 6 months. The community detection analysis revealed three possible clinical subtypes.<h4>Discussion</h4>We showed that frequency rates of PCS differ between methods and data sources, but characteristics of the affected individuals are relatively stable. Overall, PCS is a heterogeneous syndrome affecting a substantial group of individuals who need adequate care. Future studies should focus on care trajectories and qualitative measures such as quality of life of individuals living with PCS.https://doi.org/10.1371/journal.pone.0318272 |
| spellingShingle | Isabelle Bos Lisa Bosman Rinske van den Hoek Willemijn van Waarden Matthijs S Berends Maarten S Homburg Tim Olde Hartman Jean Muris Lilian S Peters Bart Knottnerus Karin S Hek Robert A Verheij Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. PLoS ONE |
| title | Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. |
| title_full | Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. |
| title_fullStr | Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. |
| title_full_unstemmed | Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. |
| title_short | Comparison of observational methods to identify and characterize post-COVID syndrome in the Netherlands using electronic health records and questionnaires. |
| title_sort | comparison of observational methods to identify and characterize post covid syndrome in the netherlands using electronic health records and questionnaires |
| url | https://doi.org/10.1371/journal.pone.0318272 |
| work_keys_str_mv | AT isabellebos comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT lisabosman comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT rinskevandenhoek comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT willemijnvanwaarden comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT matthijssberends comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT maartenshomburg comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT timoldehartman comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT jeanmuris comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT lilianspeters comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT bartknottnerus comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT karinshek comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires AT robertaverheij comparisonofobservationalmethodstoidentifyandcharacterizepostcovidsyndromeinthenetherlandsusingelectronichealthrecordsandquestionnaires |