Adverse Events Following COVID-19 Immunization Reported Through Hotlines, February-August 2021, Bangladesh: Descriptive Study
Bibliographic record
Abstract
Background For COVID-19 vaccine safety, the vaccination program of Bangladesh started facility-based passive surveillance to address adverse events following immunization (AEFIs) with COVID-19 vaccination. The Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh, has been using emergency hotlines for outbreak reporting since 2008. During the COVID-19 pandemic, these hotlines are being used for pandemic-related information and reporting. Thus, COVID-19 vaccinees also use these hotlines to report AEFIs. Objective We analyzed the documented AEFIs records of the IEDCR to characterize the vaccinees who reported AEFIs through IEDCR hotlines. Methods We performed a descriptive analysis of COVID-19 vaccinees who reported AEFIs through IEDCR hotlines from February to August 2021. We defined AEFIs as untoward medical occurrences that follow immunization and that do not necessarily have a causal relationship with the usage of the vaccines. We analyzed the vaccinees who reported AEFIs through IEDCR hotlines by age, gender, occupation, the severity of AEFIs, and the time intervals of reporting. Results Of 819 vaccinees who reported AEFIs through IEDCR hotlines, 555 (67.8%) were male and their median age was 41 years (IQR 32-51 years). Of them, 494 (89%) reported AEFIs following the first dose of vaccination. Among females, 186 (70.5%) of 264 were housewives. Among males, 249 (44.9%) of 555 were service holders, 90 (16.2%) were businessmen, and 46 (8.3%) were students. About 638 (77.9%) of 819 vaccinees were from urban vaccination centers. Mild AEFIs, such as fever (508/819, 62%), injection-site pain (336/819, 41%), and headache (205/819, 25%), were reported through IEDCR hotlines. Although 534 (65.2%) of 819 vaccinees who reported AEFIs through IEDCR hotlines developed symptoms within 24 hours of vaccination, only 196 (23.9%) of 819 vaccinees reported them within 24 hours. Conclusions Middle-aged, male, and urban vaccinees who developed mild AEFIs commonly reported AEFIs through IEDCR hotlines. We recommended that AEFI data generated from different reporting systems, including hotline numbers, be incorporated together for an efficient COVID-19 vaccine safety surveillance system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".