Ascertainment of vaccination status by self‐report versus source documentation: Impact on measuring COVID‐19 vaccine effectiveness
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, self-reported COVID-19 vaccination might facilitate rapid evaluations of vaccine effectiveness (VE) when source documentation (e.g., immunization information systems [IIS]) is not readily available. We evaluated the concordance of COVID-19 vaccination status ascertained by self-report versus source documentation and its impact on VE estimates. METHODS: Hospitalized adults (≥18 years) admitted to 18 U.S. medical centers March-June 2021 were enrolled, including COVID-19 cases and SARS-CoV-2 negative controls. Patients were interviewed about COVID-19 vaccination. Abstractors simultaneously searched IIS, medical records, and other sources for vaccination information. To compare vaccination status by self-report and documentation, we estimated percent agreement and unweighted kappa with 95% confidence intervals (CIs). We then calculated VE in preventing COVID-19 hospitalization of full vaccination (2 doses of mRNA product ≥14 days prior to illness onset) independently using data from self-report or source documentation. RESULTS: Of 2520 patients, 594 (24%) did not have self-reported vaccination information to assign vaccination group; these patients tended to be more severely ill. Among 1924 patients with both self-report and source documentation information, 95.0% (95% CI: 93.9-95.9%) agreement was observed, with a kappa of 0.9127 (95% CI: 0.9109-0.9145). VE was 86% (95% CI: 81-90%) by self-report data only and 85% (95% CI: 81-89%) by source documentation data only. CONCLUSIONS: Approximately one-quarter of hospitalized patients could not provide self-report COVID-19 vaccination status. Among patients with self-report information, there was high concordance with source documented status. Self-report may be a reasonable source of COVID-19 vaccination information for timely VE assessment for public health action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.088 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".