The impact of statistical adjustment for assay performance on inferences from SARS-CoV-2 serological surveillance studies
Bibliographic record
Abstract
Choice of immunoassay influences population seroprevalence estimates. Post hoc adjustments for assay performance could improve comparability of estimates across studies and enable pooled analyses. We assessed post hoc adjustment methods using data from 2021 to 2023 SARS-CoV-2 serosurveillance studies in Alberta, Canada: one that tested 124 008 blood donations using Roche immunoassays (SARS-CoV-2 nucleocapsid total antibody and anti-SARS-CoV-2 S) and another that tested 214 780 patient samples using Abbott immunoassays (SARS-CoV-2 IgG and anti-SARS-CoV-2 S). Comparing datasets, seropositivity for antibodies against nucleocapsid (anti-N) diverged after May 2022 due to differential loss of sensitivity as a function of time since infection. The commonly used Rogan-Gladen adjustment did not reduce this divergence. Regression-based adjustments using the assays' semiquantitative results produced more similar estimates of anti-N seroprevalence and rolling incidence proportion (proportion of individuals infected in recent months). Seropositivity for antibodies targeting SARS-CoV-2 spike protein was similar without adjustment, and concordance was not improved when applying an alternative, functional threshold. These findings suggest that assay performance substantially impacted population inferences from SARS-CoV-2 serosurveillance studies in the Omicron period. Unlike methods that ignore time-varying assay sensitivity, regression-based methods using the semiquantitative assay resulted in increased concordance in estimated anti-N seropositivity and rolling incidence between cohorts using different assays.
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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 | 0.399 | 0.590 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".