P-40. Impact of case and control selection on influenza vaccine effectiveness (VE) among adults aged 40 years and older hospitalized with acute respiratory illness (ARI) during 2022-2023 using a test negative design (TND): secondary analysis of the North America multi-specimen study
Bibliographic record
Abstract
Abstract Background Case and control definitions may impact VE estimates in observational studies. Using a TND design, we evaluated how influenza vaccine effectiveness against ARI hospitalizations changed when 1) influenza cases were detected through nasopharyngeal swabs (NPS) only versus multiple specimens and 2) controls included or excluded other vaccine preventable diseases (VPD). Methods Adults aged ≥40 years hospitalized for ARI had NPS and saliva or sputum tested using Luminex RSV/influenza multiplex PCR assay. Influenza was defined as either positive by NPS alone or positive by saliva, sputum, or NPS. Controls were defined as either all persons who were influenza negative or those negative for influenza and positive for respiratory syncytial virus (RSV). Exposure was self-reported seasonal influenza vaccination, defined as receipt of vaccine after 1 July 2022 and prior to 14 days of ARI illness onset. VE was calculated as 1 - ratio of odds of influenza vaccination among cases over controls. Results Among 2,999 patients with NPS and ≥1 other specimen, influenza detection increased by 40%, 74% and 91% when sputum, saliva, or both were added to NPS results, respectively (Table 1). Among 2,954 (98.5%) patients with influenza vaccine data, and when controls were defined as any influenza-negative ARI hospitalization, VEs were -3.6% (95%CI -49.5–28.3) and -0.9% (95% CI -32.5–23.2), using cases identified by NPS versus any specimen type, respectively (Table 2). When controls were persons who were RSV positive and influenza negative, VEs were 38.5% (95% CI -3.5–63.4) and 43.2% (95% CI 14.9–62.1) using cases identified by NPS versus any specimen type, respectively. Conclusion Adding saliva and sputum nearly doubles influenza detection compared to NPS alone but does not substantially alter VE estimates. By contrast, VE was substantially increased when defining controls as those who were RSV+, yielding a result closer to the US Centers for Disease Control and Prevention values of 39-43% for the same season. The latter increase in VE may have occurred because RSV positivity reduces confounding caused by inclusion of other vaccine preventable infections (ie, SARS-CoV-2) in the control group due to correlation between vaccination against influenza and SARS-CoV-2. Disclosures Negar Aliabadi, MD, MS, Pfizer Inc: employment|Pfizer Inc: Stocks/Bonds (Public Company) Qing Liu, M.S., Pfizer Inc.: Stocks/Bonds (Public Company) Julio A. Ramirez, MD, FACP, Pfizer: Julio Ramirez is an employee of Norton Healthcare (Louisville, KY), which received fees from Pfizer in relation to this study. Allison McGeer, MD, AstraZeneca: Honoraria|GSK: Honoraria|Merck: Honoraria|Moderna: Honoraria|Novavax: Honoraria|Pfizer: Grant/Research Support|Pfizer: Honoraria|Roche: Honoraria|Seqirus: Grant/Research Support|Seqirus: Honoraria Ruth Carrico, PhD, DNP, APRN, Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Sanofi: Advisor/Consultant|Seqirus: Advisor/Consultant Samira Mubareka, MD, Pfizer: Grant/Research Support Verna Welch, PhD, MPH, Pfizer Inc.: Stocks/Bonds (Public Company) Malak Elsobky, MD, Pfizer: Stocks/Bonds (Public Company) Bradford D. Gessner, M.D., M.P.H., Pfizer: Employee|Pfizer: Stocks/Bonds (Public Company) Elizabeth Begier, MD, M.P.H., Pfizer Vaccines: Employee|Pfizer Vaccines: Stocks/Bonds (Private Company)
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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 | 0.042 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".