Utility of Newborn Dried Blood Spots to Ascertain Seroprevalence of SARS-CoV-2 Antibodies Among Individuals Giving Birth in New York State, November 2019 to November 2021
Bibliographic record
Abstract
Importance: Serosurveys can be used to monitor population-level dynamics of COVID-19 and vaccination. Dried blood spots (DBSs) collected from infants contain maternal IgG antibodies and are useful for serosurveys of individuals recently giving birth. Objectives: To examine SARS-CoV-2 antibody prevalence in pregnant individuals in New York State, identify associations between SARS-CoV-2 antibody status and maternal and infant characteristics, and detect COVID-19 vaccination among this population. Design, Setting, and Participants: A population-based, repeated cross-sectional study was conducted to detect SARS-CoV-2 nucleocapsid (N) and spike (S) IgG antibodies. Deidentified DBS samples and data submitted to the New York State Newborn Screening Program between November 1, 2019, and November 30, 2021, were analyzed. Exposures: Prenatal exposure to SARS-CoV-2 antibodies. Main Outcomes and Measures: The presence of IgG antibodies to SARS-CoV-2 N and S antigens was measured using a microsphere immunoassay. Data were analyzed by geographic region and compared with reported COVID-19 cases and vaccinations among reproductive-aged females (15-44 years of age). Data were stratified by infant birth weight, gestational age, maternal age, and multiple birth status. Results: Dried blood spot samples from 415 293 infants (median [IQR] age, 1.04 [1.00-1.20] days; 210 805 [51.1%] male) were analyzed for SARS-CoV-2 antibodies. The first known antibody-positive infant in New York State was born on March 29, 2020. SARS-CoV-2 seroprevalence reflected statewide and regional COVID-19 cases among reproductive-aged females in the prevaccine period. From February through November 2021, S seroprevalence was strongly correlated with cumulative vaccinations in each New York State region and in the state overall (rs = 0.92-1.00, P ≤ .001). S and N seroprevalences were significantly lower in newborns with very low birth weight (720 [14.8%] for S and 138 [2.8%] for N, P < .001) and low birth weight (5160 [19.3%] for S and 1233 [4.6%] for N, P = .009) compared with newborns with normal birth weight (77 116 [20.1%] for S and 19 872 [5.2%] for N). Lower N and higher S seroprevalences were observed in multiple births (odds ratio [OR], 0.84; 95% CI, 0.75-0.94; P = .002 for N and OR, 1.24; 95% CI, 1.18-1.31; P < .001 for S) vs single births and for maternal age older than 30 years (OR, 0.87; 95% CI, 0.80-0.94; P < .001 for N and OR, 1.17; 95% CI, 1.11-1.23; P < .001 for S) vs younger than 20 years. Conclusions and Relevance: In this study, seroprevalence in newborn DBS samples reflected COVID-19 case fluctuations and vaccinations among reproductive-aged women during the study period. These results demonstrate the utility of using newborn DBS testing to estimate SARS-CoV-2 seroprevalence in pregnant individuals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".