Testing behaviour may bias observational studies of vaccine effectiveness
Bibliographic record
Abstract
Abstract Backgroun Recent observational studies have suggested that vaccines for the omicron variant of SARS-Cov2 may have little or no effect in preventing infection. However, the observed effects may be confounded by patient factors and preventive behaviours or vaccine-related differences in testing behaviour. To assess the potential degree of confounding, we aimed to estimate differences in testing behaviour between unvaccinated and vaccinated populations. Methods We recruited 1,526 Australian adults for an online randomised study about COVID testing between October and November 2021, and collected self-reported vaccination status and three measures of COVID-19 testing behaviour. We examined the association between testing intentions and vaccination status in the cross-sectional baseline data of this trial. Results Of the 1,526 participants (mean age 31 years): 22% had a COVID-19 test in the past month and 61% ever; 17% were unvaccinated, 11% were partially vaccinated (1 dose), 71% were fully vaccinated (2+ doses). Fully vaccinated participants were twice as likely (RR 2.2; 95% CI 1.8 to 2.8) to report positive COVID testing intentions than those who were unvaccinated (p<.001). Partially vaccinated participants had less positive intentions than those fully vaccinated (p<.001) but higher intentions than those who were unvaccinated (p=.002). Discussion For all three measures vaccination predicted greater COVID testing intentions. If the unvaccinated tested at half the rate of the vaccinated, a true vaccine effectiveness of 30% could appear to be a “negative” observed vaccine effectiveness of -40%. Assessing vaccine effectiveness should use methods to account for differential testing behaviours. Test negative designs are currently the preferred option, but its assumptions should be more thoroughly examined.
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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".