ArticleFrontiers in public health2024
SARS-CoV-2 seroprevalence and antibody trajectories after easing of COVID-19 restrictions: a longitudinal study in China.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: We aimed to evaluate the seroprevalence of SARS-CoV-2 and investigate the trajectories of protective immunity and associated risk factors in eastern China between March and November 2023 after the easing of COVID-19 restrictions. Materials and methods: We conducted repeated population-based seroepidemiologic studies using a multistage, population-stratified, cluster random sampling method. We measured neutralizing antibodies (nAbs) using a fluorescence immunoassay. We calculated both overall and stratified seroprevalence. The latent class growth mixed model (LCGMM) was used to analyze the dynamic trajectories of antibodies, and a multinomial logistic regression model was used to identify factors associated with different antibody trajectory patterns. Results: A total of 6,147 participants were included at baseline, with a median age of 53.61 years. Both observed and adjusted seroprevalence remained high and stable throughout the study period. The LCGMM identified four distinct antibody trajectories: 75.22% of participants had a high and stable antibody trajectory, while nearly 8% of them exhibited an increase, decline, or low-stable antibody trajectory. Younger participants, women, those fully vaccinated, and individuals with a history of previous infection were more likely to have high and stable antibody trajectories. Conclusion: The majority of the population maintained sustained protective immunity after the outbreak, following the easing of COVID-19 restrictions across the country.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.