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Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media

BACKGROUND: Long COVID—a condition with persistent symptoms post COVID-19 infection—is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and...

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Autores principales: Déguilhem, Amélia, Malaab, Joelle, Talmatkadi, Manissa, Renner, Simon, Foulquié, Pierre, Fagherazzi, Guy, Loussikian, Paul, Marty, Tom, Mebarki, Adel, Texier, Nathalie, Schuck, Stephane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685517/
https://www.ncbi.nlm.nih.gov/pubmed/36447795
http://dx.doi.org/10.2196/39849
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author Déguilhem, Amélia
Malaab, Joelle
Talmatkadi, Manissa
Renner, Simon
Foulquié, Pierre
Fagherazzi, Guy
Loussikian, Paul
Marty, Tom
Mebarki, Adel
Texier, Nathalie
Schuck, Stephane
author_facet Déguilhem, Amélia
Malaab, Joelle
Talmatkadi, Manissa
Renner, Simon
Foulquié, Pierre
Fagherazzi, Guy
Loussikian, Paul
Marty, Tom
Mebarki, Adel
Texier, Nathalie
Schuck, Stephane
author_sort Déguilhem, Amélia
collection PubMed
description BACKGROUND: Long COVID—a condition with persistent symptoms post COVID-19 infection—is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data. OBJECTIVE: We aimed to identify and analyze Long Haulers’ main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. METHODS: Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users’ messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term’s total was used to create user-level hierarchal clusters. RESULTS: Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year. CONCLUSIONS: Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers’ quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers’ voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID.
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spelling pubmed-96855172022-11-25 Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media Déguilhem, Amélia Malaab, Joelle Talmatkadi, Manissa Renner, Simon Foulquié, Pierre Fagherazzi, Guy Loussikian, Paul Marty, Tom Mebarki, Adel Texier, Nathalie Schuck, Stephane JMIR Infodemiology Original Paper BACKGROUND: Long COVID—a condition with persistent symptoms post COVID-19 infection—is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data. OBJECTIVE: We aimed to identify and analyze Long Haulers’ main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. METHODS: Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users’ messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term’s total was used to create user-level hierarchal clusters. RESULTS: Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year. CONCLUSIONS: Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers’ quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers’ voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID. JMIR Publications 2022-11-22 /pmc/articles/PMC9685517/ /pubmed/36447795 http://dx.doi.org/10.2196/39849 Text en ©Amélia Déguilhem, Joelle Malaab, Manissa Talmatkadi, Simon Renner, Pierre Foulquié, Guy Fagherazzi, Paul Loussikian, Tom Marty, Adel Mebarki, Nathalie Texier, Stephane Schuck. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 22.11.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Déguilhem, Amélia
Malaab, Joelle
Talmatkadi, Manissa
Renner, Simon
Foulquié, Pierre
Fagherazzi, Guy
Loussikian, Paul
Marty, Tom
Mebarki, Adel
Texier, Nathalie
Schuck, Stephane
Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title_full Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title_fullStr Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title_full_unstemmed Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title_short Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media
title_sort identifying profiles and symptoms of patients with long covid in france: data mining infodemiology study based on social media
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9685517/
https://www.ncbi.nlm.nih.gov/pubmed/36447795
http://dx.doi.org/10.2196/39849
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