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.