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Record W4321597125 · doi:10.3389/fpubh.2023.1043696

Pattern of self-reported adverse events related to COVID-19 vaccines in Saudi Arabia: A nationwide study

2023· article· en· W4321597125 on OpenAlexaff
Joud Mohammed Alkhalifah, Ahad Al Seraihi, Jaffar A. Al‐Tawfiq, Badr Fadhel Alshehri, Alhanouf Hani Alhaluli, Naif Mansour Alsulais, Mohammed Mesfer Alessa, Waleed Seddiq, Thamer Aljeri, Mohammad Hassan Qahtani, Mazin Barry

Bibliographic record

VenueFrontiers in Public Health · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAdverse effectNauseaVaccinationPediatricsEmergency medicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Background Vaccination against coronavirus disease 2019 (COVID-19) is the most effective way to end the pandemic. Any development of adverse events (AEs) from various vaccines should be reported. We therefore aimed to explore major and minor AEs among vaccinated individuals in Saudi Arabia. Methods This is a nationwide report based on the Saudi Arabian Ministry of Health (MOH) registry. It included those who received COVID-19 vaccines from 17th December 2020 to 31st December 2021. The study included spontaneous self-reported adverse effects to COVID-19 vaccines where the study participants used a governmental mobile app (Sehhaty) to report their AEs following vaccination using a checklist option that included a selection of side-effects. The primary outcome was to determine AEs reported within 14 days of vaccination which included injection site itching, pain, reaction, redness, swelling, anxiety, dizziness, fever, headache, hoarseness, itchiness, loss of consciousness, nausea, heartburn, sleep disruption, fatigue, seizures, anaphylaxis, shortness of breath, wheezing, swelling of lips, face, and throat, loss of consciousness, and admissions into the intensive care unit (ICU). Results The study included a total number of 28,031 individuals who reported 71,480 adverse events (AEs); which were further classified into minor and major adverse events including ICU admissions post vaccination. Of the reported AEs, 38,309 (53. 6%) side-effects were reported following Pfizer-BioNTech, 32,223 (45%) following Oxford-AstraZeneca, and 948 (1.3%) following Moderna. The following reported AEs were statistically significant between the different vaccine types: shortness of breath\difficulty of breathing, dizziness, fever above 39°C, headache, hoarseness, injection site reactions, itchiness, nausea, sleep disruption, fatigue, wheezing, swelling of lips/face and\or throat, and loss of consciousness (p-value < 0.05). Fever and seizure were the only statistically significant AEs amongst the number of vaccine doses received (p-value < 0.05). Ten ICU admissions were reported in the 14 days observation period post-COVID-19 vaccination with the following diagnoses: acute myocardial infarction, pneumonia, atherosclerosis, acute respiratory failure, intracranial hemorrhage, grand mal seizure, Guillain-Barré syndrome, abnormal blood gas levels, and septic shock. Conclusion This study demonstrated that the most prevalent SARS-CoV-2 vaccine side-effects among adults in Saudi Arabia were mild in nature. This information will help reduce vaccine hesitancy and encourage further mass vaccination to combat the COVID-19 pandemic, especially as booster doses are now available. Further studies are warranted to obtain a better understanding of the association between risk factors and the experiencing of side-effects post vaccination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.395
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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