Exploring indirect protection associated with influenza immunization – A systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Influenza causes significant annual morbidity and mortality, particularly in older adults, for whom influenza vaccine effectiveness (VE) is also lower. Immunizing one group (e.g., children) against influenza may indirectly protect another group (e.g., older adults) against influenza and its complications. METHODS: We updated previous systematic reviews on indirect protection against influenza by searching MEDLINE and EMBASE for relevant human studies published until January 4, 2017. We abstracted and critically appraised English language publications that reported or provided information to calculate indirect VE against influenza, as a percentage, in non-institutional settings. We developed a term called 'estimated actual protection' to explore the relationship between indirect protection and the product of direct VE and relative vaccine coverage. We calculated estimated actual protection for a subset of studies that reported coverage and indirect VE for: laboratory-confirmed influenza; outpatient care for respiratory illness; influenza-associated emergency visits; or influenza-associated hospitalizations. We ran linear mixed models to compare estimated actual protection against indirect VE for the four outcomes, and graphed the data. RESULTS: Of 2320 unique records identified, we abstracted and appraised 26 articles describing 24 studies. The majority of included studies reported at least one outcome suggesting that immunizing one group reduced influenza-related outcomes in another group. Critical appraisal of the abstracted studies identified recurring methodological weaknesses, such as lack of laboratory-confirmed influenza. Our exploratory analyses of 18 studies indicated a positive but not statistically significant relationship between estimated actual protection and indirect protection for each of the four outcomes. CONCLUSIONS: Our systematic review and exploratory analyses suggest influenza immunization provides some level of indirect protection. However, our critical appraisal highlights the need for a standardized and consistently applied approach to measuring indirect protection against influenza to fill existing knowledge gaps. Additionally, the concept of estimated actual protection requires validation.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".