Effects of COVID-19 vaccination and past SARS-CoV-2 infection on subsequent COVID-19 infection in people with HIV
Bibliographic record
Abstract
BACKGROUND: This study aims to estimate the time-varying effects of primary and booster COVID-19 vaccination and past SARS-CoV-2 infection on subsequent SARS-CoV-2 infection (including new infection and re-infection) in people with HIV (PWH). METHODS: A population-based cohort was retrieved from the integrated statewide HIV electronic health record (EHR) dataset, COVID-19 vaccination dataset, and COVID-19 diagnoses dataset between March 2, 2020 and April 14, 2022. The pre-specified outcome was any SARS-CoV-2 infection. We used Cox regression to estimate the time-varying effects of primary and booster vaccination and past infection on the risks of subsequent SARS-CoV-2 infection. RESULTS: A total of 18,509 eligible PWH who had documentation of COVID-19 testing or COVID-19 vaccination records were included for analysis. The effectiveness of primary vaccination against infection, compared with being unvaccinated, was relatively low (26.70 %, 95 % CI: 12.10 %, 38.88 %) at two months, while the effectiveness of a booster dose after two months was high (43.53 %, 95 %CI: 27.54 %, 55.99 %), compared with primary vaccination only (e.g., first two doses of Pfizer or Moderna, or the single dose of Janssen). The effectiveness of past COVID-19 infection during Pre-Delta and Delta dominant periods at one month against reinfection was (67.43 %; 95 %CI: 52.74 %, 77.55 %) and (64.57 %; 95 %CI: 1.39 %, 87.27 %), respectively. CONCLUSION: Natural immunity conferred from past COVID-19 infection in PWH against reinfection appeared to be higher than vaccine-induced immunity. Boosters were more effective than the primary series alone in preventing subsequent infection.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".