Seropositivity to SARS-CoV-2 in Alberta, Canada in a post-vaccination period (March 2021–July 2021)
Bibliographic record
Abstract
Background The COVID-19 pandemic has necessitated the need to rapidly make public health decisions. We systematically evaluated SARS-CoV-2 seropositivity to understand local COVID-19 epidemiology and support evidence-based public health decision making.Methods Residual blood samples were collected for SARS-CoV-2 receptor binding domain (RBD) IgG testing over a 1–5 day period monthly from 26 February 2021–9 July 2021 from six clinical laboratories across the province of Alberta, Canada. Monthly crude and adjusted (for age and gender) seropositivity were calculated. Results were linked to provincial administrative, laboratory, and vaccine databases.Results 60,632 individual blood samples were tested. Vaccination data were available for 98.8% of samples. Adjusted RBD IgG positivity rose from 11.9% (95% confidence interval [CI] 11.9–12.0%) in March 2021 to 70.2% (95% CI 70.2–70.3%) in July 2021 (p < .0001). Seropositivity rose from 9.4% (95% CI 9.3–9.4%) in March 2021 to 20.2% (95% CI 20.1–20.2%) in July 2021 in unvaccinated Albertans. Unvaccinated seropositive individuals were from geographic areas with significantly (p < .001) lower median household income, lower proportion of married/common-law relationships, larger average household size and higher proportions of visible minorities compared to seronegative unvaccinated individuals. In July 2021, the age groups with the lowest and highest seropositivity in unvaccinated Albertans were those ≥80 years (12.0%, 95% CI 5.3–18.6%) and 20–29 years (24.2%, 95% CI 19.6–28.8%), respectively. Of seropositive unvaccinated individuals, 50.2% (95% CI 45.9–54.5%) had no record of prior SARS-CoV-2 molecular testing.Conclusions Longitudinal surveillance of SARS-CoV-2 seropositivity with data linkage is valuable for decision-making during the pandemic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".