Testing behaviour may bias observational studies of vaccine effectiveness
Bibliographic record
Abstract
BACKGROUND: Recent observational studies suggest that vaccines may have little effect in preventing infection with the Omicron variant of severe acute respiratory syndrome coronavirus 2. However, the observed effects may be confounded by patient factors, preventive behaviours, or differences in testing behaviour. To assess potential confounding, we examined differences in testing behaviour between unvaccinated and vaccinated populations. METHODS: We recruited 1,526 Australian adults for an online randomized study about coronavirus disease 2019 (COVID-19) testing in late 2021, collecting self-reported vaccination status and three measures of COVID-19 testing behaviour: testing in past month or ever and test intention if they woke with a sore throat. We examined the association between testing intentions and vaccination status in the trial’s baseline data. RESULTS: Of the 1,526 participants (mean age 31 y), 22% had a COVID-19 test in the past month and 61% ever; 17% were unvaccinated, 11% were partially vaccinated (one dose), and 71% were fully vaccinated (two or more doses). Fully vaccinated participants were twice as likely as those who were unvaccinated (relative risk [RR] 2.2, 95% CI 1.8 to 2.8, p < 0.001) to report positive COVID testing intentions. Partially vaccinated participants had less positive intentions than fully vaccinated participants (RR 0.68, 95% CI 0.52 to 0.89, p < 0.001) but higher intentions than unvaccinated participants (RR 1.5, 95% CI 1.4 to 1.6, p = 0.002). DISCUSSION: Vaccination predicted greater COVID-19 testing intentions and would substantially bias observed vaccine effectiveness. To account for differential testing behaviours, test-negative designs are currently the preferred option, but their assumptions need more thorough examination.
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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 | 0.287 | 0.604 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".