Evidence map›Paper›PMID 33758898›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Serological surveys to estimate cumulative incidence of SARS-CoV-2 infection in adults (Sero-MAss study), Massachusetts, July-August 2020: a mail-based cross-sectional study.

Teah Snyder, Johanna Ravenhurst, Estee Y Cramer, Nicholas G Reich, Laura B Balzer, Dominique Alfandari, Andrew A Lover

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Teah Snyder *Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
Johanna Ravenhurst *Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
Estee Y Cramer *Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
Nicholas G ReichDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
Laura B BalzerDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
Dominique AlfandariDepartment of Veterinary and Animal Sciences, College of Natural Sciences, University of Massachusetts- Amherst, Amherst MA.
Andrew A LoverDepartment of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts- Amherst, Amherst MA.
University of Massachusetts Amherst · US

Funding

Production and characterization of monoclonal antibodies to Xenopus ProteinsR24OD021485 · OD · UNIVERSITY OF MASSACHUSETTS AMHERST · PI ALFANDARI, DOMINIQUE R · 2017 to 2020
$1.2M
NIH HHS R24 OD021485
6 · The paper itself

Abstract

backgroundThe SARS-CoV-2 pandemic is an unprecedented global health crisis. The state of Massachusetts was especially impacted during the initial stages; however, the extent of asymptomatic transmission remains poorly understood due to limited asymptomatic testing in the "first wave." To address this gap, a geographically representative and contact-free seroprevalence survey was conducted in July-August 2020, to estimate prior undetected SARS-CoV-2 infections.

methodsStudents, faculty, librarians and staff members at the University of Massachusetts, Amherst without a previous COVID-19 diagnosis were invited to participate in this study along with one member of their household in June 2020. Two separate sampling frames were generated from administrative lists: all undergraduates and their household members (primary sampling group) were randomly selected with probability proportional to population size. All staff, faculty, graduate students and librarians (secondary sampling group) were selected as a simple random sample. After informed consent and a socio-behavioral survey, participants were mailed test kits and asked to return self-collected dried blood spot (DBS) samples. Samples were analyzed via ELISA for anti-SARS-CoV-2 IgG antibodies, and then IgM antibodies if IgG-positive. Seroprevalence estimates were adjusted for survey non-response. Binomial models were used to assess factors associated with seropositivity in both sample groups separately.

resultsApproximately 27,000 persons were invited via email to assess eligibility. Of the 1,001 individuals invited to participate in the study, 762 (76%) returned blood samples for analysis. In the primary sampling group 548 returned samples, of which 230 enrolled a household member. Within the secondary sampling group of 214 individuals, 79 enrolled a household member. In the primary sample group, 36 (4.6%) had IgG antibodies detected for an estimated weighed prevalence for this population of 5.3% (95% CI: 3.5 to 8.0). In the secondary sampling group, 10 (3.4%) of 292 individuals had IgG antibodies detected for an estimated adjusted prevalence of 4.0% (95% CI: 2.2 to 7.4). No samples were IgM positive. No association was found in either sample group between seropositivity and self-reported work duties or customer-facing hours. In the primary sampling group, self-reported febrile illness since Feb 2020, male sex, and minority race (Black or American Indian/Alaskan Native) were associated with seropositivity. No factors except geographic regions within the state were associated with evidence of prior SARS-CoV-2 infection in the secondary sampling group.

interpretationThis study provides insight into the seroprevalence of university-related populations and their household members across the state of Massachusetts during the summer of 2020 of the pandemic and helps to fill a critical gap in estimating the levels of sub-clinical and asymptomatic infection. Estimates like these can be used to calibrate models that estimate levels of population immunity over time to inform public health interventions and policy.

Identifiers

PMID33758898
PMCPMC7987057
OpenAlexW3134267396

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.