Trends in SARS-CoV-2 seroprevalence in Massachusetts estimated from newborn screening specimens
Bibliographic record
Abstract
Abstract Background Estimating the cumulative incidence of SARS-CoV-2 is essential for setting public health policies. We leveraged de-identified Massachusetts newborn screening specimens to generate an accessible, retrospective source of maternal antibodies for estimating statewide SARS-CoV-2 seroprevalence in a non-test-seeking population. Methods We analyzed 72,117 newborn dried blood spots collected from November 2019 through December 2020, representing 337 towns and cities across Massachusetts. Seroprevalence was estimated for the general Massachusetts population after correcting for imperfect test specificity and nonrepresentative sampling using Bayesian multilevel regression and poststratification. Results Statewide seroprevalence was estimated to be 0.03% (90% credible interval (CI) [0.00, 0.11]) in November 2019 and rose to 1.47% (90% CI [1.00, 2.13]) by May 2020, following sustained SARS-CoV-2 transmission in the spring. Seroprevalence plateaued from May onwards, reaching 2.15% (90% CI [1.56, 2.98]) in December 2020. Seroprevalence varied substantially by community and was particularly associated with community percent non-Hispanic Black (β = 0.024, 90% CI [0.004, 0.044]); i.e., a 10% increase in community percent non-Hispanic Black was associated with a 27% higher odds of seropositivity. Seroprevalence estimates had good concordance with reported case counts and wastewater surveillance for most of 2020, prior to the resurgence of transmission in winter. Conclusions Cumulative incidence of SARS-CoV-2 protective antibody in Massachusetts was low as of December 2020, indicating that a substantial fraction of the population was still susceptible. Maternal seroprevalence data from newborn screening can inform longitudinal trends and identify cities and towns at highest risk, particularly in settings where widespread diagnostic testing is unavailable. Summary The measurement of maternal antibodies in dried blood spots collected for newborn screening offers a statewide source of SARS-CoV-2 seroprevalence data independent of case testing limitations. We analyzed 72,117 Massachusetts spots collected November 2019 – December 2020 and estimated longitudinal trends.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".