Impact of dried blood spot vs. venous sample collection on SARS-CoV-2 antibody test results in the CLSA serological study of older Canadians
Bibliographic record
Abstract
Participant-collected dried blood spots (DBS), which can be returned using regular mail, are convenient for collecting samples for population serology studies. The impact of DBS sampling on assay performance remains unclear. We estimated the impact of using DBS samples on estimates of population SARS-CoV-2 antibody levels within a study of older Canadians. Analyzing data from 3,796 participants who provided DBS samples and 3,457 participants who provided venous samples, we estimated the impact of sample collection modality on odds of testing positive for anti-nucleocapsid antibodies (Anti-N) or for antibodies to the SARS-CoV-2 spike protein (Anti-S) using the Elecsys Anti-SARS-CoV-2 and Anti-SARS-CoV-2 S immunoassays. We used inverse probability of treatment weighting to control for differences between participants who provided DBS or venous samples, including sample collection date and participants’ age, vaccination status, and geographic location. Compared to venous samples, DBS samples were less likely to be Anti-N positive (weighted risk ratio [RR]: 0.30, 95% confidence interval [CI]: 0.25–0.36) and less likely to be Anti-S positive (RR: 0.83, 95% CI: 0.80–0.86). We estimate that using DBS for all participants would have underestimated seropositivity for anti-N antibodies (2.48% using DBS; 8.32% using venous) and anti-S antibodies by (42.7% using DBS; 51.6% using venous). In population serology studies, DBS samples should only be used instead of venous blood draws when the impact on assay performance is well-characterized and appropriate adjustments are used to derive population seropositivity estimates.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".