Analysis of Answers to Queries among Anonymous Users with Gastroenterological Problems on an Internet Forum

Kamińśki, Mikołaj; Borger, Michał; Prymas, Piotr; Muth, Agnieszka; Stachowski, A; Łoniewski, Igor; Marlicz, Wojciech

doi:10.3390/ijerph17031042

Cited by 7 publications

(7 citation statements)

References 50 publications

(55 reference statements)

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“…Injection site irritations (local reactions) were an order of magnitude less likely to be reported than fever and pain (systemic reaction). Gastric symptoms (especially diarrhea, with a frequency of 0.6% per report) were less likely to be reported than the average prevalence among the general population (1%-5% for diarrhea [ 45 , 46 ]).…”

Section: Resultsmentioning

confidence: 99%

“…These symptoms could be a taboo effect [ 70 ], such as a response to public speaking anxiety. Alternatively, it could be easily ignored because of its high prevalence, or it could be eliminated using an over-the-counter medicine such as loperamide [ 45 , 46 ]. Insomnia was detected so often that it suggests an epidemiological link with the vaccine, which needs further investigation.…”

Section: Discussionmentioning

confidence: 99%

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Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Jarynowski¹,

Semenov²,

Kamińśki³

et al. 2021

J Med Internet Res

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Background There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Jarynowski¹,

Semenov²,

Kamińśki³

et al. 2021

J Med Internet Res

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show abstract

“…The analysis of web-derived data provides insights into the under-researched fields of medicine [13]. The most popular source of e-data on health issues are search engine statistics, social media [14], e-forums [15], and websites [16]. The interest of Google users in various dietary supplements has increased in the last years [17].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Websites Presenting Parenteral Supplementation Services in Five European Countries: A Cross-Sectional Study

et al. 2020

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We aimed to characterize the parenteral supplementation services in Czechia, Ireland, Italy, Poland, and the United Kingdom based on their websites. We generated a list of websites by searching Google using the term “vitamin infusion” and selected cities with 250,000 citizens from each analyzed country. All search inputs were performed using the native language. Data on the features of services, indications, contraindications, offered parenteral supplements, and social media activity were obtained. We analyzed 317 websites representing 371 active facilities. Only 6 (1.9%) facilities cited the scientific sources on parenteral supplementation, but these reference were highly biased; 17.4% did not provide information regarding their personnel, while 11.9% indicated the different contraindications. The most common indications were fatigue (62.5%), immunity enhancement (58.0%), anti-aging, and physical activity (51.5%). Approximately, 11.6% of facilities claimed that some parenteral supplements can help manage certain malignancies, while 2.2% claimed that they can help manage fertility problems. The most offered intravenous supplements were vitamins C (57.4%), B12 (47.7%), and B6 (42.3%). The parenteral supplementation market offers numerous ingredients as treatment for general health problems and serious health conditions. Many analyzed websites lacked essential information, which creates concerns for regarding the quality and reliability of the services.

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“…These symptoms could be a taboo effect [53], such as a response to public speaking anxiety. Alternatively, it could be easily ignored because of its high prevalence, or it could be eliminated using an over-the-counter medicine such as loperamide [54,55]. Insomnia was detected so often that it suggests an epidemiological link with the vaccine, which needs further investigation.…”

Section: Discussionmentioning

confidence: 99%

Mild Adverse Events of Sputnik V Vaccine Extracted from Russian Language Telegram Posts via BERT Deep Learning Model

Jarynowski

Semenov

Kaminski³

et al. 2021

Preprint

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Background: There is a limited amount of data on the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V) safety profile. Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective: We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Materials and Methods: We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multi-label classifications using the deep neural language model BERT DeepPavlov, which we pre-trained on a Russian language corpus and applied to the Telegram messages. The resulting AUC score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results: The results of the retrospective analysis showed that females reported more AEs than males (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.13-fold, P<.001), and the number of AEs decreased with age (β = .05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) compared with mRNA ones (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase III clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=.94, P=.02) with those reported in the Argentinian post-marketing AE registry. Conclusion: After receiving the Sputnik V vaccination, Telegram users complained about pain (47%), fever (47%), fatigue (34%), and headache (25%). The results showed that the AE profile of Sputnik V was comparable with other COVID-19 vaccines. Examining the sentinel properties of participatory trials (which is subject to self-reporting biases) could still provide meaningful information about pharmaceutics, especially if only a limited amount of information on AEs is provided by producers.

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Analysis of Answers to Queries among Anonymous Users with Gastroenterological Problems on an Internet Forum

Cited by 7 publications

References 50 publications

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning

Characteristics of Websites Presenting Parenteral Supplementation Services in Five European Countries: A Cross-Sectional Study

Mild Adverse Events of Sputnik V Vaccine Extracted from Russian Language Telegram Posts via BERT Deep Learning Model

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