Sociodemographic characteristics of SARS-CoV-2 serosurveillance studies with diverse recruitment strategies, Canada, 2020 to 2023
Bibliographic record
Abstract
ABSTRACT Background Serological testing was a key component of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) surveillance. Social distancing interventions, resource limitations, and the need for timely data led to serosurveillance studies using a range of recruitment strategies, which likely influenced study representativeness. Characterizing representativeness in surveillance is crucial to identify gaps in sampling coverage and to assess health inequities. Methods We retrospectively analyzed three pre-existing longitudinal cohorts, two convenience samples using residual blood, and one de novo probabilistic survey conducted in Canada between April 2020 – November 2023. We calculated study specimen counts by age, sex, urbanicity, race/ethnicity, and neighborhood deprivation quintiles. We derived a ‘representation ratio’ as a simple metric to assess generalizability to a target population and various sociodemographic strata. Results The six studies included 1,321,675 specimens. When stratifying by age group and sex, 65% of racialized minority subgroups were moderately underrepresented (representation ratio < 0.75). Representation was generally higher for older Canadians, urban neighborhoods, and neighborhoods with low material deprivation. Rural representation was highest in a study that used outpatient laboratory blood specimens. Racialized minority representation was highest in a de novo probabilistic survey cohort. Conclusion While no study had adequate representation of all subgroups, less traditional recruitment strategies were more representative of some population dimensions. Understanding demographic representativeness and barriers to recruitment are important considerations when designing population health surveillance studies.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".