Evidence map›Paper›PMID 38012125›Full record

ArticleAmerican journal of epidemiology2024

Correcting for Antibody Waning in Cumulative Incidence Estimation From Sequential Serosurveys.

Sarah Kadelka, Judith A Bouman, Peter Ashcroft, Roland R Regoes

Open access · hybridAbstract read
In one paragraph

Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Sarah Kadelka
Judith A Bouman
Peter Ashcroft
Roland R Regoes
ETH Zurich · CHInstitute of Social and Preventive Medicine · CHNovartis Institutes for BioMedical Research

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Serosurveys are a widely used tool to estimate the cumulative incidence-the fraction of a population that has been infected by a given pathogen. These surveys rely on serological assays that measure the level of pathogen-specific antibodies. Because antibody levels are waning, the fraction of previously infected individuals that have seroreverted increases with time past infection. To avoid underestimating the true cumulative incidence, it is therefore essential to correct for waning antibody levels. We present an empirically supported approach for seroreversion correction in cumulative incidence estimation when sequential serosurveys are conducted in the context of a newly emerging infectious disease. The correction is based on the observed dynamics of antibody titers in seropositive cases and validated using several in silico test scenarios. Furthermore, through this approach we revise a previous cumulative incidence estimate relying on the assumption of an exponentially declining probability of seroreversion over time, of severe acute respiratory syndrome coronavirus 2, of 76% in Manaus, Brazil, by October 2020 to 47.6% (95% confidence region: 43.5-53.5). This estimate has implications, for example, for the proximity to herd immunity in Manaus in late 2020.

Indexed as

COVID-19SARS-CoV-2Antibodies, ViralBrazilHumansIncidenceSeroepidemiologic StudiesAntibodies, Viralcumulative incidenceinfectious diseaseSARS-CoV-2serosurveys

Identifiers

PMID38012125
PMCPMC11074712
OpenAlexW4389068264

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.