Frailty Reduces Vaccine Effectiveness Against SARS-CoV-2 Infection: A Test-Negative Case Control Study Using National VA Data
Bibliographic record
Abstract
OBJECTIVES: To assess the variation of vaccine effectiveness against SARS-CoV-2 infection during the Delta wave according to frailty status among U.S. veterans. DESIGN: Test-negative case-control study of SARS-CoV-2 mRNA vaccine effectiveness. SETTING: Veterans Health Administration (VHA) medical centers. PARTICIPANTS: Veterans 19 years and older who had at least one COVID-19/Flu like symptoms and received a SARS-CoV-2 PCR or antigen test at VHA medical centers between July 25 to September 30, 2021. INTERVENTION: mRNA vaccination. MEASUREMENTS: New SARS-CoV-2 infection. Vaccine effectiveness was defined as 1-odds of vaccination in cases/odds of vaccination in controls, where cases were patients who had a COVID-19 test and tested positive for SARS-CoV-2, and controls were those who tested negative. Frailty was measured using the VA frailty index, categorized as robust (0-<0.1), pre-frail (≥0.1-<0.21) and frail (≥0.21). RESULTS: A total of 58,604 patients (age:58.9±17.0, median:61, IQR:45-72; 87.5%men; 68.1%white; 1.3%African American, 8.3%Hispanic) were included in the study. Of these, 27,733 (47.3%) were robust, 16,276 (27.8%) were prefrail, and 14,595 (24.9%) were frail. mRNA vaccine effectiveness against the Delta variant symptomatic infection was lower in patients with frailty, 62.8 %(95%CI:59.8-65.7), versus prefrail 73.9%(95%CI:72.0-75.7), and robust, 77.0 %(95%CI:75.7-78.3). CONCLUSIONS: This test-negative case control study showed that mRNA vaccine effectiveness against infection declined in veterans with frailty. Frailty status is a factor to consider when designing, developing, and evaluating COVID-19 vaccines.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".