Surveillance System Evaluation for COVID-19 Vaccine–Associated Adverse Events Following Immunization (AEFI), Sindh Pakistan (2021)
Bibliographic record
Abstract
Background In February 2021, a mass vaccination campaign commenced in Sindh Province in response to the COVID-19 epidemic. An adverse-events-following-immunization surveillance system (AEFI-SS) was established to monitor the adverse events following vaccination. Objective We evaluated the AEFI-SS with the aim to identify its strengths and weaknesses and suggest recommendations. Methods In May-June 2021, a descriptive evaluation study was conducted in Sindh Province, Pakistan. The Centers for Disease Control and Prevention’s (CDC) updated guidelines for evaluation of SS-2001 were followed to measure the qualitative, quantitative, and utility attributes of the AEFI-SS. Key stakeholders were identified based on their involvement in the AEFI-SS and were interviewed. Case investigation proformas for the AEFI were randomly reviewed for data quality, timeliness, and completeness. Sensitivity was calculated. Each attribute was rated as good, fair, or poor based on a scoring legend. Results The SS was useful in effectively identifying 7147 cases of AEFIs. Timeliness of all AEFI cases was good and was found to be 100%, as all cases were reported within 24 hours. The World Health Organization (WHO)-approved case definition was used for the identification of AEFI cases and had a simple flow of information. The AEFI-SS was good in data quality and completeness (100%), and data collection tools were filled by trained medical officers. Sensitivity was 100%, and the predictive value positive (PVP) was not calculated due to the absence of a laboratory component. Good representativeness (>80%) of the population was covered by 1004 vaccination centers. The system was found to be stable as resources of the health department government of Sindh were being used. The AEFI-SS was paper based and deficient in a feedback mechanism. Conclusions Sindh Province has an appropriate surveillance mechanism for AEFI detection and management for the ongoing COVID-19 vaccination SS. The representativeness can be increased by involvement of the private health sector. Establishment of a feedback mechanism and digital data transformation and integration of the AEFI system with the Expanded Program on Immunization (EPI) are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".