Assessing the Sensitivity of the Canadian Adverse Event Following Immunization Surveillance System ( CAEFISS)
Bibliographic record
Abstract
Background Background: Vaccines are important to public health, but because of the way they are manufactured, their mechanism of action, and their indicated population, careful monitoring of their adverse events is necessary.Canada has a national surveillance system that collects reports on adverse events that may be associated with vaccine administration.Sensitivity is one of the tools used with surveillance systems to study the extent and characteristics of reporting of a surveillance system.To date, the sensitivity of the Canadian system has not been assessed. Purpose Purpose: To assess the sensitivity of the Canadian Adverse Event Following Immunization Surveillance System (CAEFISS).Methods Methods: Based on specific adverse events following immunization (AEFI) and vaccines chosen for the study, a thorough literature search was completed to find the best source which identifies expected rates of AEFI.Studies used were assessed based on quality and sample size.The expected rates of AEFI, in combination with public health estimates of vaccine coverage rates, were used to estimate the expected number of reports.The reports provided the actual number of events used to calculate the sensitivity.Sensitivity was compared based on year of administration, age group, and type of AEFI.Results: Results: The overall sensitivity of the CAEFISS varied from 1.0% to 136.6% for various AEFI for the years 1997 to 2008.For influenza the sensitivity was found to be 93.6% and 136.3% for GBS and anaphylaxis respectively.For DTaP, the rates were found to be 15.0%, 1.0%, and 21.2% for anaphylaxis, HHE, and seizures respectively, and for MMR the rates were 16.5%, 52.7%, and 12.7% in relation to anaphylaxis, thrombocytopenia, and seizures respectively.Conclusions: Conclusions: This is the first assessment of the sensitivity of the CAEFISS, and this study found that the system has reasonable ability to detect AEFI on a national level.CAEFISS had comparable senstivity to other vaccine reporting systems.Many of the AEFI had sensitivity values higher than the 5%-10% range traditionally seen in other passive surveillance systems related to adverse events.The greatest variation of sensitivity was seen between vaccines.Rarity and timing of the AEFI may also impact the sensitivity.Variation of sensitivity and the variation found in the sensitivity analysis lend to the further development and implementations of case definitions for rarer adverse events, especially anaphylaxis.Further research of other factors that impact reporting is necessary.Many thanks to my advisor, Dr. Wang.She was extremely helpful throughout this process and guided me well.She has always responded quickly too all of my questions, at all hours.I would also like to thank my committee members, Dr. Arnold and Dr. Hoffman.They were more than flexible to schedule the times to propose and defend my thesis and provided great input as to the direction of my thesis.I would also like to show my deepest gratitude to Dr. Law, who supported me whole-heartedly although she met with an unexpected workload
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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 | 0.030 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".