Timeliness of reporting of SARS-CoV-2 seroprevalence results and their utility for infectious disease surveillance

Abstract

Seroprevalence studies have been used throughout the COVID-19 pandemic to monitor infection and immunity. These studies are often reported in peer-reviewed journals, but the academic writing and publishing process can delay reporting and thereby public health action. Seroprevalence estimates have been reported faster in preprints and media, but with concerns about data quality. We aimed to (i) describe the timeliness of SARS-CoV-2 serosurveillance reporting by publication venue and study characteristics and (ii) identify relationships between timeliness, data validity, and representativeness to guide recommendations for serosurveillance efforts. We included seroprevalence studies published between January 1, 2020 and December 31, 2021 from the ongoing SeroTracker living systematic review. For each study, we calculated timeliness as the time elapsed between the end of sampling and the first public report. We evaluated data validity based on serological test performance and correction for sampling error, and representativeness based on the use of a representative sample frame and adequate sample coverage. We examined how timeliness varied with study characteristics, representativeness, and data validity using univariate and multivariate Cox regression. We analyzed 1844 studies. Median time to publication was 154 days (IQR 64-255), varying by publication venue (journal articles: 212 days, preprints: 101 days, institutional reports: 18 days, and media: 12 days). Multivariate analysis confirmed the relationship between timeliness and publication venue and showed that general population studies were published faster than special population or health care worker studies; there was no relationship between timeliness and study geographic scope, geographic region, representativeness, or serological test performance. Seroprevalence studies in peer-reviewed articles and preprints are published slowly, highlighting the limitations of using the academic literature to report seroprevalence during a health crisis. More timely reporting of seroprevalence estimates can improve their usefulness for surveillance, enabling more effective responses during health emergencies.

Keywords: Bibliometrics; COVID-19; Infectious disease; Public health surveillance; Reporting; Seroprevalence.

Graphical abstract

Fig. 1

Kaplan-Meier curve and risk table for time-to-publication by publication venue. Pairwise comparisons indicate significant differences in timeliness between publication venues. Media and institutional reports were published significantly faster than all other publication venues (all log-rank p < 2e-16, with Bonferroni correction). Preprints were published in significantly shorter time compared to journal articles (log-rank p < 2e-16) and presentation or conference materials (log-rank p = 0.003). Presentation and conference materials were also released faster than journal articles (log-rank p = 0.049). Timeliness curves are plotted with 95 % confidence intervals (shaded area).

Fig. 2

Kaplan-Meier curves for timeliness across study characteristics. Comparison of study timeliness according to (A) the WHO region the study was conducted in (reference: AMRO; overall p = 0.002), (B) overall risk of bias (reference: low; overall p = 8e-14), (C) sample frames (reference: household and community samples; overall p = <2e-16) and (D) geographic scope (reference: national; overall p = 0.3). Timeliness curves are plotted with 95 % confidence intervals (shaded area).

Fig. 3

Kaplan-Meier curves for timeliness across measures of study representativeness and data quality. Comparison of timeliness according to (A) whether or not the sample was representative of the general population (overall p = 9e-04), (B) sample coverage (overall p = 0.01), (C) sensitivity and specificity of the antibody test used (overall p = 5e-05) and (D) appropriateness of sampling method and statistical analysis (overall p = 4e-04). The reference group was “yes” for all analyses. Timeliness curves are plotted with 95 % confidence intervals (shaded area).