Prevalence of anti-SARS-CoV-2 antibodies among blood donors in Northern Cape, KwaZulu-Natal, Eastern Cape, and Free State provinces of South Africa in January 2021.
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Abstract
Background:
Population-level estimates of prevalence of anti-SARS-CoV-2 antibody positivity (seroprevalence)
is a crucial epidemiological indicator for tracking the Covid-19 epidemic. Such data
are in short supply, both internationally and in South Africa. The South African blood
services (the South African National Blood Service, SANBS and the Western Cape Blood
Service, WCBS) are coordinating a nationally representative survey of blood donors,
which it is hoped can become a cost-effective surveillance method with validity for
community-level seroprevalence estimation.
Methods:
Leveraging existing arrangements, SANBS human research ethics committee permission
was obtained to test blood donations collected on predefined days (7
th, 10
th, 12
th, 15
th, 20
th, 23
th and 25
th January) for anti-SARS-CoV-2 antibodies, using the Roche Elecsys Anti-SARS-CoV-2
assay on the cobas e411 platform currently available in the blood services’ donation
testing laboratories. Using standard methods, prevalence analysis was done by province,
age and race, allowing age to be regarded as either a continuous or categorical variable.
Testing was performed in the Eastern Cape (EC), Free State (FS), KwaZulu Natal (ZN)
and Northern Cape (NC) provinces.
Results:
We report on data from 4858 donors - 1457 in EC; 463 in NC; 831 in FS and 2107 in
ZN. Prevalence varied substantially across race groups and between provinces, with
seroprevalence among Black donors consistently several times higher than among White
donors, and the other main population groups (Coloured and Asian) not consistently
represented in all provinces. There is no clear evidence that seroprevalence among
donors varies by age. Weighted net estimates of prevalence (in the core age range
15–69) by province
(compared with official clinically-confirmed COVID-19 case rates in mid-January 2021) are: EC-63%
(2.8%), NC-32%
(2.2%), FS-46%
(2.4%), and ZN-52%
(2.4%).
Conclusions:
Our study demonstrates substantial differences in dissemination of SARS-CoV-2 infection
between different race groups, most likely explained by historically based differences
in socio-economic status and housing conditions. As has been seen in other areas,
even such high seroprevalence does not guarantee population-level immunity against
new outbreaks – probably due to viral evolution and waning of antibody neutralization.
Despite its limitations, notably a ‘healthy donor’ effect, it seems plausible that
these estimates are reasonably generalisable to actual population level anti-SARS-CoV-2
seroprevalence, but should be further verified.
After initially containing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many European and Asian countries had a resurgence of COVID-19 consistent with a large proportion of the population remaining susceptible to the virus after the first epidemic wave. 1 By contrast, in Manaus, Brazil, a study of blood donors indicated that 76% (95% CI 67–98) of the population had been infected with SARS-CoV-2 by October, 2020. 2 High attack rates of SARS-CoV-2 were also estimated in population-based samples from other locations in the Amazon Basin—eg, Iquitos, Peru 70% (67–73). 3 The estimated SARS-CoV-2 attack rate in Manaus would be above the theoretical herd immunity threshold (67%), given a basic case reproduction number (R0) of 3. 4 In this context, the abrupt increase in the number of COVID-19 hospital admissions in Manaus during January, 2021 (3431 in Jan 1–19, 2021, vs 552 in Dec 1–19, 2020) is unexpected and of concern (figure ).5, 6, 7, 8, 9, 10 After a large epidemic that peaked in late April, 2020, COVID-19 hospitalisations in Manaus remained stable and fairly low for 7 months from May to November, despite the relaxation of COVID-19 control measures during that period (figure). Figure COVID-19 hospitalisations, excess deaths, and Rt in Manaus, Brazil, 2020–21 (A) Dark lines are the 7-day rolling averages and lighter lines are the daily time series of COVID-19 hospitalisations and excess deaths. Hospitalisation data are from the Fundação de Vigilância em Saúde do Amazonas. 5 Total all-cause deaths for 2020–21 were reported initially by the Prefeitura de Manaus 6 and subsequently in the daily COVID-19 bulletin of the Fundação de Vigilância em Saúde do Amazonas. 7 All-cause deaths from 2019 were from Arpen/AM (Associação dos Registradores Civis das Pessoas Naturais do Amazonas). 8 The compiled excess death data are from Bruce Nelson from the Instituto Nacional de Pesquisas da Amazônia. 9 (B) R t was calculated using the time series of COVID-19 hospitalisations after removal of the past 14 days to account for delays in notification. R t was calculated using the EpiFilter method. 10 Lines are median R t estimates; shaded areas are the 95% CIs. R t=Effective reproduction number. SARS-CoV-2=severe acute respiratory syndrome coronavirus 2. There are at least four non-mutually exclusive possible explanations for the resurgence of COVID-19 in Manaus. First, the SARS-CoV-2 attack rate could have been overestimated during the first wave, and the population remained below the herd immunity threshold until the beginning of December, 2020. In this scenario, the resurgence could be explained by greater mixing of infected and susceptible individuals during December. The 76% estimate of past infection 2 might have been biased upwards due to adjustments to the observed 52·5% (95% CI 47·6–57·5) seroprevalence in June, 2020, to account for antibody waning. However, even this lower bound should confer important population immunity to avoid a larger outbreak. Furthermore, comparisons of blood donors with census data showed no major difference in a range of demographic variables, 2 and the mandatory exclusion of donors with symptoms of COVID-19 is expected to underestimate the true population exposure to the virus. Reanalysis and model comparison 11 by independent groups will help inform the best-fitting models for antibody waning and the representativeness of blood donors. Second, immunity against infection might have already begun to wane by December, 2020, because of a general decrease in immune protection against SARS-CoV-2 after a first exposure. Waning of anti-nucleocapsid IgG antibody titres observed in blood donors 2 might reflect a loss of immune protection, although immunity to SARS-CoV-2 depends on a combination of B-cell and T-cell responses. 12 A study of UK health-care workers 13 showed that reinfection with SARS-CoV-2 is uncommon up to 6 months after the primary infection. However, most of the SARS-CoV-2 infections in Manaus occurred 7–8 months before the resurgence in January, 2021; this is longer than the period covered by the UK study, 13 but nonetheless suggests that waning immunity alone is unlikely to fully explain the recent resurgence. Moreover, population mobility in Manaus decreased from mid-November, 2020, with a sharp reduction in late December, 2020, 14 suggesting that behavioural change does not account for the resurgence of hospitalisations. Third, SARS-CoV-2 lineages might evade immunity generated in response to previous infection. 15 Three recently detected SARS-CoV-2 lineages (B.1.1.7, B.1.351, and P.1), are unusually divergent and each possesses a unique constellation of mutations of potential biological importance.16, 17, 18 Of these, two are circulating in Brazil (B.1.1.7 and P.1) and one (P.1) was detected in Manaus on Jan 12, 2021. 16 One case of SARS-CoV-2 reinfection has been associated with the P.1 lineage in Manaus 19 that accrued ten unique spike protein mutations, including E484K and N501K. 16 Moreover, the newly classified P.2 lineage (sublineage of B.1.128 that independently accrued the spike E484K mutation) has now been detected in several locations in Brazil, including Manaus. 20 P.2 variants with the E484K mutation have been detected in two people who have been reinfected with SARS-CoV-2 in Brazil,21, 22 and there is in-vitro evidence that the presence of the E484K mutation reduces neutralisation by polyclonal antibodies in convalescent sera. 15 Fourth, SARS-CoV-2 lineages circulating in the second wave might have higher inherent transmissibility than pre-existing lineages circulating in Manaus. The P.1 lineage was first discovered in Manaus. 16 In a preliminary study, this lineage reached a high frequency (42%, 13 of 31) among genome samples obtained from COVID-19 cases in December, 2020, but was absent in 26 samples collected in Manaus between March and November, 2020. 16 Thus far, little is known about the transmissibility of the P.1 lineage, but it shares several independently acquired mutations with the B.1.1.7 (N501Y) and the B.1.325 (K417N/T, E484K, N501Y) lineages circulating in the UK and South Africa, which seem to have increased transmissibility. 18 Contact tracing and outbreak investigation data are needed to better understand relative transmissibility of this lineage. The new SARS-CoV-2 lineages may drive a resurgence of cases in the places where they circulate if they have increased transmissibility compared with pre-existing circulating lineages and if they are associated with antigenic escape. For this reason, the genetic, immunological, clinical, and epidemiological characteristics of these SARS-CoV-2 variants need to be quickly investigated. Conversely, if resurgence in Manaus is due to waning of protective immunity, then similar resurgence scenarios should be expected in other locations. Sustained serological and genomic surveillance in Manaus and elsewhere is a priority, with simultaneous monitoring for SARS-CoV-2 reinfections and implementation of non-pharmaceutical interventions. Determining the efficacy of existing COVID-19 vaccines against variants in the P.1 lineage and other lineages with potential immune escape variants is also crucial. Genotyping viruses from COVID-19 patients who were not protected by vaccination in clinical trials would help us to understand if there are lineage-specific frequencies underlying reinfection. The protocols and findings of such studies should be coordinated and rapidly shared wherever such variants emerge and spread. Since rapid data sharing is the basis for the development and implementation of actionable disease control measures during public health emergencies, we are openly sharing in real-time monthly curated serosurvey data from blood donors through the Brazil–UK Centre for Arbovirus Discovery, Diagnosis, Genomics and Epidemiology (CADDE) Centre GitHub website and will continue to share genetic sequence data and results from Manaus through openly accessible data platforms such as GISAID and Virological.
Highlights • Improved serological detection of specific antibodies against SARS-CoV-2 can help estimate the true number of infections. • The seroprevalence can vary according to different sites and the seroprevalence can increase with time in the longitudinal follow-up. • The seroprevalence of healthcare workers wearing adequate personal protective equipment is thought to not be higher than other groups. • Seroprevalence can vary according to different populations, such as pregnant women and hemodialysis patients
Admixture is a well known confounder in genetic association studies. If genome-wide data is not available, as would be the case for candidate gene studies, ancestry informative markers (AIMs) are required in order to adjust for admixture. The predominant population group in the Western Cape, South Africa, is the admixed group known as the South African Coloured (SAC). A small set of AIMs that is optimized to distinguish between the five source populations of this population (African San, African non-San, European, South Asian, and East Asian) will enable researchers to cost-effectively reduce false-positive findings resulting from ignoring admixture in genetic association studies of the population. Using genome-wide data to find SNPs with large allele frequency differences between the source populations of the SAC, as quantified by Rosenberg et. al's -statistic, we developed a panel of AIMs by experimenting with various selection strategies. Subsets of different sizes were evaluated by measuring the correlation between ancestry proportions estimated by each AIM subset with ancestry proportions estimated using genome-wide data. We show that a panel of 96 AIMs can be used to assess ancestry proportions and to adjust for the confounding effect of the complex five-way admixture that occurred in the South African Coloured population.
[2)
]DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch
University
[3)
]Western Cape Blood Service
[4)
]Vitalant Research Institute
[5)
]University of California San Francisco
[6)
]University of Cape Town
[7)
]University of the Free State
Article
Other ID: 10.21203/rs.3.rs-233375
DOI: 10.21203/rs.3.rs-233375/v1
PMC ID: 7885925
PubMed ID: 33594353
SO-VID: f640488f-e639-4e1d-ab7c-e88df23b6a35
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