Global SARS-CoV-2 seroprevalence: a systematic review and meta-analysis of standardized population-based studies from Jan 2020-May 2022
Notice bibliographique
Résumé
Abstract Background Our understanding of the global scale of SARS-CoV-2 infection remains incomplete: routine surveillance data underestimates infection and cannot infer on population immunity, there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in WHO’s Unity protocol for general population seroepidemiological studies, two years into the pandemic, to estimate the extent of population infection and remaining susceptibility. Methods and Findings We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between 2020-01-01 and 2022-05-20. The review protocol is registered with PROSPERO, (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studies - those aligned with the WHO Unity protocol - were extracted and critically appraised in duplicate, with Risk of Bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate under-ascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. The main limitations of our methodology include that some estimates were driven by certain countries or populations being over-represented. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% LMIC) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/sub-national scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1-62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6-28.8] to 86.7% [84.6-88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3-11.0%] to 95.9% [92.6-97.8%] in Europe high-income countries in December 2021). After the emergence of Omicron, infection-induced seroprevalence rose to 47.9% [41.0-54.9%] in EUR HIC and 33.7% [31.6-36.0%] in AMR HIC in March 2022. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0-9 years and adults 60+ were at lower risk of seropositivity than adults 20-29 ( p <0.0001 and p =0.005, respectively). In a multivariable model using pre-vaccination data, stringent public health and social measures were associated with lower seroprevalence ( p =0.02). Conclusions In this study, we observed that global seroprevalence has risen considerably over time and with regional variation, however around 40 % of the global population remains susceptible to SARS-CoV-2 infection. Our estimates of infections based on seroprevalence far exceed reported COVID-19 cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,022 | 0,005 |
| Bibliométrie | 0,000 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».