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Meta-Analysis
. 2022 Aug;7(8):e008793.
doi: 10.1136/bmjgh-2022-008793.

SARS-CoV-2 infection in Africa: a systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021

Hannah C Lewis  1   2 , Harriet Ware  3 , Mairead Whelan  3 , Lorenzo Subissi  2 , Zihan Li  4 , Xiaomeng Ma  5 , Anthony Nardone  2   6 , Marta Valenciano  2   6 , Brianna Cheng  2   7 , Kim Noel  8 , Christian Cao  3 , Mercedes Yanes-Lane  8   9 , Belinda L Herring  10 , Ambrose Talisuna  10 , Nsenga Ngoy  10 , Thierno Balde  10 , David Clifton  11 , Maria D Van Kerkhove  2 , David Buckeridge  8   12 , Niklas Bobrovitz  13 , Joseph Okeibunor  10 , Rahul K Arora  3   11 , Isabel Bergeri  2 ; UNITY Studies Collaborator Group
Collaborators, Affiliations
Meta-Analysis

SARS-CoV-2 infection in Africa: a systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021

Hannah C Lewis et al. BMJ Glob Health. 2022 Aug.

Abstract

Introduction: Estimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence studies to estimate SARS-CoV-2 seroprevalence in Africa to inform evidence-based decision making on public health and social measures (PHSM) and vaccine strategy.

Methods: We searched for seroprevalence studies conducted in Africa published 1 January 2020-30 December 2021 in Medline, Embase, Web of Science and Europe PMC (preprints), grey literature, media releases and early results from WHO Unity studies. All studies were screened, extracted, assessed for risk of bias and evaluated for alignment with the WHO Unity seroprevalence protocol. We conducted descriptive analyses of seroprevalence and meta-analysed seroprevalence differences by demographic groups, place and time. We estimated the extent of undetected infections by comparing seroprevalence and cumulative incidence of confirmed cases reported to WHO.

Prospero: CRD42020183634.

Results: We identified 56 full texts or early results, reporting 153 distinct seroprevalence studies in Africa. Of these, 97 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% (95% CI 1.0% to 9.2%) in April-June 2020 to 65.1% (95% CI 56.3% to 73.0%) in July-September 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 100:1, ranging from 18:1 to 954:1) and steady over time. Seroprevalence was highly heterogeneous both within countries-urban versus rural (lower seroprevalence for rural geographic areas), children versus adults (children aged 0-9 years had the lowest seroprevalence)-and between countries and African subregions.

Conclusion: We report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and potential protection against COVID-19 severe disease than indicated by surveillance data. As seroprevalence was heterogeneous, targeted PHSM and vaccination strategies need to be tailored to local epidemiological situations.

Keywords: COVID-19; epidemiology; public health; serology; systematic review.

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Conflict of interest statement

Competing interests: WHO had a role in study design, data collection, data analysis, data interpretation and writing of the report. No other funders had any such role. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. Authors were not precluded from accessing data in the study, and they accept responsibility to submit for publication. Potential other competing interests of named coauthors include: RKA reports personal fees from the Public Health Agency of Canada and the Bill and Melinda Gates Foundation Strategic Investment Fund, as well as equity in Alethea Medical (outside the submitted work). MPC reports personal fees from GEn1E Lifesciences (outside the submitted work), nplex biosciences (outside the submitted work) and Kanvas biosciences (outside the submitted work). JP reports grants from MedImmune (outside the submitted work) and Sanofi-Pasteur (outside the submitted work), grants and personal fees from Merck (outside the submitted work) and AbbVie (outside the submitted work) and personal fees from AstraZeneca (outside the submitted work). CCo reports funding from Sanofi Pasteur (outside of the submitted work). KE reports being cochairman of ANRS group on public health and social science in France (outside of the submitted work). JDS reports consulting fees from ASLM, GIZ health-focus and l’Oréal (all outside of the submitted work).

Figures

Figure 1
Figure 1
PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Figure 2
Figure 2
Reported seroprevalence, variants of concern and cumulative incidence by selected countries over time, March 2020–December 2021. Plots for all countries are reported in online supplemental figure S2. Top panel, left axis: shaded areas represent the relative frequency of major variants of concern (VOCs) circulating, based on weekly counts of hCoV-19 genomes submitted to GISAID (Global Initiative on Sharing Avian Influenza Data) that we have aggregated by month. Weeks with fewer than 10 total submissions in a given country were excluded from the analysis. Top panel, right axis: daily confirmed cases reported to WHO on a national level per million people, smoothed using local regression (Locally Estimated Scatterplot Smoothing, LOESS). Middle panel: each point is an individual seroprevalence study, and identical shapes represent studies originating from the same cohort or repeated cross-sectional data source. Bottom panel: cumulative incidence of confirmed cases per 100 people.
Figure 3
Figure 3
Pooled seroprevalence from infection or vaccination (random-effects model) by UN subregion and quarter, Q3 2020–Q3 2021. Point estimates and 95% CIs (error bars) are reported for each quarter, as well as the number of samples pooled (n).
Figure 4
Figure 4
Factors associated with seroprevalence. Top panel: meta-analysis results. We calculated the ratio in prevalence between subgroups within each study then aggregated the ratios across studies using inverse variance-weighted random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic. Each row represents a separate meta-analysis. Bottom panel: multivariable analysis using a Poisson generalised linear mixed-effects regression to model seroprevalence. CLIA, chemiluminescent immunoassay; cumulative incidence, cumulative incidence of confirmed cases per 100 people; ELISA, enzyme-linked immunosorbent assay; LFIA, lateral flow immunoassay; PR, prevalence ratio.
See this image and copyright information in PMC

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