The Pregnancy Vaccine Effectiveness Network (PREVENT): Establishing a Multi-Country Cohort to Estimate Vaccine Effectiveness (VE) against Hospitalized Influenza During Pregnancy
Bibliographic record
Abstract
Pregnant women are at greater risk of complications from influenza (flu) infection than the general population. Although vaccination is an effective method to prevent influenza, the vaccine is underutilized during pregnancy. A challenge to maternal flu vaccination is the paucity of data about the effectiveness of inactivated influenza vaccines (IIV) in preventing severe outcomes in pregnant women. To inform policy and address this knowledge gap, CDC developed a multi-country collaboration to investigate the preventive value of IIV during pregnancy during multiple flu seasons. We present the progress to date of this Network. PREVENT was established in April 2016 to: i) estimate incidence of influenza and vaccination rates; ii) describe epidemiologic characteristics associated with illness; and iii) estimate IIV effectiveness in preventing hospitalizations during pregnancy associated with RT PCR -confirmed influenza. We selected sites that could identify the population of women known to be pregnant during flu seasons and integrate their hospitalization data, clinical laboratory testing, and vaccination records. We will assess VE using the case test-negative control design and use meta-analyses to pool VE estimates across sites and account for significant differences. Primary analyses will be completed by August 2017. Seven sites in Australia, Canada, Israel, and the US were selected; a protocol and data dictionary were finalized. We identified 1,024 pregnant women hospitalized with acute respiratory illness and RT-PCR tested, during six influenza seasons (2010–11 through 2015–16). Of the qualifying women, 550 (54%) tested positive for flu. Positivity varied by site (range 41% (US)–61.8% (Ontario, CAN)), and vaccination coverage varied across sites and seasons (range 7.3% (Ontario, CAN)–46% (US)). Analyses will examine flu season characteristics, vaccination patterns, and clinical and birth outcomes related to respiratory illness during pregnancy and flu incidence. Laboratory-confirmed influenza hospitalization during pregnancy is a relatively low-frequency event. Pooling data across multiple sites offers a way to estimate VE against severe influenza outcomes in pregnant women that is informative to influenza vaccine policy. A. Naleway, MedImmune: Investigator, Research grant; Pfizer: Investigator, Research grant; Merck: Investigator, Grant recipient; N. P. Klein, GSK: Investigator, Research grant; sanofi pasteur: Investigator, Grant recipient; Merck & Co: Investigator, Grant recipient; MedImmune: Investigator, Grant recipient; Protein Science: Investigator, Research grant; Pfizer: Investigator, Grant recipient; S. Irving, Medimmune/AstraZeneca: Investigator, Research support
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".