Discussion
Overall, we found that an estimated 10.5% of Cameroonian survey
participants aged ≥5 years were SARS-CoV-2 seropositive at the end of
2020. SARS-CoV-2 seroprevalence ranged widely by region (7.5% to
12.4%) and seroprevalence was higher among males, persons older than 25
years of age, those who reported ever having tested positive for
SARS-CoV-2 and among those who had reported having a recently traveled.
However, seroprevalence did not differ by, residential setting (urban
vs. rural), having a comorbid condition, or household
size.19 Another seroprevalence survey conducted in
Cameroon that sampled households in Yaoundé, Cameroon that was conducted
around the same time as this study found a seroprevalence of 29.2%
(95% CI 24·3–34·1).20 This was more than 2.5 times
higher than what we found for the seroprevalence (11.8%) in the Center
region where Yaoundé is located. Like our survey, this survey also found
that seroprevalence was higher among males. However, unlike our survey,
they found a higher seroprevalence among participants living in larger
households.
Our finding of 10.5% seroprevalence translates to an estimated
2,347,500 total infections across Cameroon at the time of the survey.
This is over 100 times higher than the cumulative number of cases
reported as of December 30, 2020.18 Currently, there
are 124,392 all time confirmed cases and 1,965 deaths reported in
Cameroon due to SARs-CoV-240. A meta-analysis that
used data from seroprevalence studies conducted in 2020 found an overall
seroprevalence of 19.5% in Sub-Saharan Africa which varied widely and
was significantly higher than that in high-income
countries.21 The authors also found that
seroprevalence estimates were a median 18.1 times higher than the
cumulative incidence of reported cases overall and 600 times higher in
Sub-Saharan Africa. Another meta-analysis which used data published
through December 2021 from Africa only (including data from our survey)
also found a very high ratio (97:1) of seroprevalence to cumulative
incidence that remained fairly constant over time. Several reasons have
been suggested for the seemingly low number of cases and even lower
mortality rate and the misalignment between seroprevalence findings and
reported cases in Africa. These factors include lack of access to health
services, including SARS-CoV-2 testing; limited public health
surveillance capacity and infrastructure, including shortages of
SARS-CoV-2 real-time (RT)-PCR test kits and other laboratory supplies; ;
swift and wide-reaching public health measures established by many
countries; and the overall lower age of its
population.23-24 Further, in 2020, the stigma
associated with COVID-19, along with misinformation and disinformation
in the community likely resulted in testing avoidance which exacerbated
the undercounting of cases.25
As SARS-CoV-2 can spread asymptomatically, the official reported number
of cases to health reporting systems globally did not include all the
possible infections. This underscored the need for seroprevalence
surveys that could present a full picture of the disease burden in a
population by measuring SARS-CoV-2 antibodies in sampled blood specimens
to detect previous infection, regardless of the presence or absence of
symptoms. In countries where testing numbers were low, both because of
low demand and testing supply shortages, the need for serosurveys was
critical to understanding the epidemic both on a national and
sub-national level, which was the case for Cameroon. Thus, we designed
the first SARS-CoV-2 seroprevalence survey that included all 10 regions
of Cameroon comprising over 10,000 adults and children aged ≥5 years. In
each city, individuals were recruited from multiple sites to increase
the diversity of participants. The survey also demonstrated the
feasibility of performing a community-based serological survey in large
African regional centers.
The prevalence estimates from this study were based on two assays:
WANTAI SARS-CoV-2 Ab ELISA and Abbott Architect SARS-CoV-2 IgG. The
WANTAI assay, which was made available by the WHO and was not
independently evaluated at the time of the survey while the Abbott assay
was authorized for use by the U.S. FDA after independently qualifying
the assay with 100% sensitivity and 99.6%
specificity.26 In 2020, many antibody test kits
entered the market with variable, and in some cases unreliable, test
performance characteristics (sensitivity, specificity, PPV and
NPV).27 To overcome some of these test kit performance
issues, we employed a parallel two-test algorithm which increased the
overall PPV for more accurate seroprevalence estimates. This approach
differed substantially from that used by the majority of serosurveys
conducted in the early days of the pandemic whereby a single test was
employed to estimate prevalence, leading to uneven surveillance data
quality.7,28 Some of these antibody tests included
lateral flow immunoassays that had poorer performance than ELISA
assays.29
In our parallel two-test algorithm, we found poor concordance between
the WANTAI and Abbott assays (Kappa value = 0.19), with the WANTAI assay
producing a very high positivity rate of 45.9% compared to that of the
Abbott assay, which was 14.3%. Similar to our findings, other studies
have noted high positivity rates with the WANTAI assay compared to other
ELISA assays.30-32 One possible explanation for this
discrepancy is that the WANTAI assay detects total antibodies to the
receptor binding domain (RBD) of the SARS-CoV-2 spike protein while the
Abbott assay only detects IgG antibodies against the N protein. Assays
that detect total antibodies have been shown to be more sensitive than
those that detect either IgM or IgG and detect antibody earlier in
infections (<21 days post-symptom
onset).33-35 Another possible contribution to the high
positivity is the lower specificity due to cross-reactivity to other
circulating antibodies resulting from past infections from other
pathogens. This has been noted in other evaluations, including WHO’s own
evaluation,36 resulting in false-positives and higher
overall prevalence estimates.37 Conversely, the lower
positivity rate from the Abbott assay may be due to the lower
sensitivity associated with only detecting IgG antibodies and timing of
testing from days post infection (<21 days). Given the
shortcomings of both assays, as well as the unknown prevalence of
COVID-19 in Cameroon at the time of the survey, a parallel testing
algorithm was used to reduce false positives and improve the overall PPV
of the SARS-CoV-2 prevalence estimates for this survey.
This survey had several limitations. First, the use of convenience
sampling from the regional capitals may indicate that the results are
not representative of the entire population of Cameroon. However, the
use of age stratified targets and post-stratification with the
participant weights was used to help reduce the effects of this
selection bias. Second, bias may have been introduced because of
experiences with COVID-19, for example, people may have been more
willing to participate if they or someone they know had been affected by
COVID-19, or they may have been less willing, if they felt like they had
already contracted COVID-19 and were not interested in finding out their
antibody statuses. Third, the assays could have missed individuals who
were still in the early stages of seroconversion. Our survey also has
several strengths. It was the first SARS-CoV-2 seroprevalence survey
that included all 10 regions of Cameroon and included over 10,000 adults
and children aged ≥5 years. In each city, individuals were recruited
from multiple sites to increase the diversity of participants. The
survey also demonstrated the feasibility of performing a community-based
serological survey in large African urban centers. Further, we used two
antibody tests to increase both PPV and NPV.
We conducted our survey in late 2020 before Cameroon experienced its
second largest SARS-CoV-2 wave and two subsequent waves that likely
increased seroprevalence,18 especially given that only
3% of the population had been vaccinated by the end of that
year.38 Repeated seroprevalence surveys after each
large infection wave would have been useful to understand how readily
the virus spreads in this population, and also the impact of vaccines on
the spread of the virus and any associated mortality.