Introduction

COVID-19 remains a global public health concern and is especially pernicious in regions with limited public health infrastructure that suffer from inadequate epidemiologic surveillance and delayed implementation of pandemic countermeasures. In the Central Asian states, such as Kazakhstan, substantial underestimations (of ~14-fold) of COVID-19 incidence and associated mortality [1–3] have led to public distrust and slow uptake of public health measures, including vaccination [4]. To date, the extent of community exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Kazakhstan is incompletely understood. Thus, as of 7 August 2021 (when analysis presented in this work was completed [5]), the officially reported number of all-time COVID-19 cases in Kazakhstan was 689,402 (626,402 of which were PCR-confirmed, while the rest were diagnosed based on clinical disease manifestations). These figures represented a cumulative prevalence of ~3.7% [6]- a prevalence that appears low given the substantial excess of infections and mortality estimated for Kazakhstan consistent with COVID-19 [2, 3].

Disparities between reported cases and true infections occur due to a plethora of factors, including unreported asymptomatic and mild infections, limited access to timely clinical and laboratory confirmation of COVID-19 diagnosis, and false-negative laboratory test results [7]. Case underestimation varies broadly by country and is most pronounced in lower income regions. Similar to its neighbouring Central Asian and Eastern European states, Kazakhstan’s healthcare and vital registration systems have struggled to keep a consistent tally of COVID-19 incidence and mortality. This was especially evident at the onset of the pandemic in Spring-Summer of 2020 when cases were counted solely if COVID-19 was identified as the main cause of hospitalization and/or death, resulting in a significant undercounting of undiagnosed pneumonia cases, which most likely were COVID-19-associated [1, 3, 8].

One way to estimate the proportion of the population with previous exposure to COVID-19 is by using serological surveillance [7], which has been under-utilized in Kazakhstan and other Central Asian states. Here, we wished to gain insight into the true SARS-CoV-2 exposure rates in the Karaganda district of Kazakhstan, a multi-ethnic region, inhabited predominantly by ethnic Kazakhs (~60%), and other ethnic groups with diverse Slavic and Eastern European and Central Asian backgrounds. Therefore, we assessed full-length SARS-CoV-2 Spike (S)-specific IgG and IgA titres in a public university-based cohort, representing a diverse array of people with different risks of exposure to COVID-19.

Materials and methods

Results

All 100 participants tested negative for SARS-CoV-2 by RT-qPCR at screening. Of 100 participants, 42 (42.0%; 95% CI [32.2–52.3]) were S-IgG+. Due to insufficient sample volume, we were unable to include two samples (one IgG+ and one IgG-) in IgA testing, thus out of the 98 participants tested for IgA, 58 (59.2%; 95% CI [48.8–69.0]) were S-IgA+ (Fig 1A). None of the pre-pandemic samples were positive for S-IgG or -IgA (Fig 1A).

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Fig 1.

a) Distribution of optic density (OD) 450 ratios for blood SARS-CoV-2 Spike (S)-reactive IgG and IgA among the study participants recruited in Spring 2021 (n = 100) compared to the pre-pandemic samples obtained in 2016 (n = 10). b) Correlation plot of SARS-CoV-2 Spike-reactive IgG and IgA levels among the 2021 study participants (n = 98). In a) and b) the red dotted lines represent the assay cut-off values at OD450 ratios = 0.8 for positive samples for both IgG and IgA. c) The SARS-CoV-2 neutralization capacity of the 2021 study participant plasma samples measured using the surrogate virus neutralization test. N = 25 in the No Prior COVID group, and N = 30 in the Prior COVID group. The red dotted line represents the assay cut-off value at 30% for positive samples. d) Correlations between SARS-CoV-2 neutralization versus S-reactive IgG and IgA among the study participants with a serology-confirmed history of COVID-19 (the Prior COVID-19 group, N = 30).

https://doi.org/10.1371/journal.pone.0272008.g001

When stratified by the presence/absence of both IgG and IgA, there were 37 (37.8%) IgG+/IgA+, 4 (4.1%) IgG+/IgA-, 21 (21.4%) IgG-/IgA+ and 36 (36.7%) IgG-/IgA- subjects (Fig 1B). Cumulatively there were 63.6% (63/99; 95% CI [53.4–73.1]) subjects positive for at least one of the antibodies (including one participant with a missing S-IgA test results); these subjects were defined as the "Prior COVID" group (Table 1).

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Table 1. Demographic and clinical characteristics of the study cohort.

https://doi.org/10.1371/journal.pone.0272008.t001

To further characterize the serologic features of the cohort, we compared the levels of SARS-CoV-2 binding antibodies and the capacity of participant plasma to neutralize SARS-CoV-2 RBD-hACE2 interaction in vitro. We found that S-IgG and S-IgA levels correlated strongly across the cohort (r = 0.599, p<0.001, Fig 1B). The SARS-CoV-2 neutralization capacity was significantly higher in the Prior COVID group compared to the No Prior COVID group (p<0.001, Fig 1C). In the Prior COVID group, 17 out of 35 tested participants (48.6%) exhibited neutralization exceeding the assay positivity cut-off (Fig 1C). SARS-CoV-2 neutralization also significantly correlated with circulating S-reactive IgG in the Prior COVID group (Fig 1D).

Lastly, we compared the clinical and demographic features of the cohort stratified by the serology-confirmed COVID-19 exposure. The Prior COVID group consisted of 25% more people self-identifying as ethnic Kazakhs (p = 0.022), and more frequently self-reported having had a respiratory illness since March 2020 (p<0.001), compared to the No prior COVID group (Table 1). Only in a minority of cases (24%, 8/33) self-reported respiratory illness was confirmed as COVID-19 by PCR and/or serology at the time of sickness; most of the self-reported respiratory illness occurred in March-Aug 2020, and in two cases in February-March 2021. There were no other significant clinical or demographic differences between the Prior COVID and No prior COVID groups (Table 1).

Discussion

Here we present anti-S IgG and IgA-based seroprevalence findings from a cohort of public university employees in Karaganda, Kazakhstan, who were invited to participate in the study prior to receiving their first dose of COVID-19 vaccine. The cohort seropositivity for anti-S IgG and IgA was 41% and 59%, respectively, while 64% of the subjects tested positive for at least one of the antibodies, thus approaching the 67% seroprevalence threshold thought to be required for establishing herd immunity [12, 13]. Consistent with studies of excess infection and death in Kazakhstan [2, 3], the serologically assessed SARS-CoV-2 exposure in this cohort was 14-15-fold higher than the reported all-time national and regional COVID-19 prevalence.

The substantial discrepancy between serology-derived and officially reported COVID-19 exposure estimates is not uncommon across the globe [7]. However, what is most striking about our findings is the unusually high SARS-CoV-2 seroprevalence exceeding the estimates for many other countries [7], albeit on par with the recent estimates from the neighbouring St. Petersburg, Russia, where antibodies against the receptor binding domain of the SARS-CoV-2 spike were detectable in ~45% of randomly sampled adults [14]. Similarly high SARS-CoV-2 seroprevalence rates have also been observed in the general populations of Brazil (>76% [15]), Ecuador (~45% [16]), India and Pakistan (>52% [17, 18]), and in communities at risk, such as healthcare workers and nursing home residents, across the globe [7]. Consistently, a recent analysis of serology data from a private laboratory network in Kazakhstan obtained for a period of 12 months (July 2020- July 2021) indicated a test positivity rate of 63% for SARS-CoV-2 IgG [19]. At the same time, a recently published household survey conducted across three cities in Kazakhstan between October 2020 and January 2021 found SARS-CoV-2 IgG/IgM-based seroprevalence rates to range from 39–61% [20].

In our cohort, serologically confirmed exposure to COVID-19 was associated with self-documented history of respiratory illness, most of which was dated by the participants to the peak of the first COVID-19 wave in the Spring-Summer of 2020 [1], period during which the country’s healthcare system was overwhelmed and laboratory testing was limited [3]. This timing of self-reported illness suggests that anti-S immunoglobulins remain detectable up to a year after symptomatic COVID-19, consistent with the established long-term persistence of SARS-CoV-2-reactive antibodies [21, 22].

COVID-19 exposure in this cohort was significantly associated with Kazakh ethnicity, consistent with our earlier finding that Kazakh people are more likely have a laboratory-confirmed COVID-19 diagnosis but are less likely to develop severe disease compared to other ethnic groups in Kazakhstan [1]. Somewhat unexpectedly, we did not see any association between the serologically confirmed exposure to COVID-19 and the participants’ professional occupation or any demographic factors. This may be because differences between sub-populations with different COVID-19 exposure risk are overwhelmed by the high seroprevalence in the general population. Alternatively, the risk of infection for healthcare workers may not be elevated relative to the general population because of the adequacy of infection control practices that are in place in healthcare facilities.

Given the logistical difficulties with procuring biomedical reagents and limited technological capacity in the setting of Kazakhstan, our choice of the serologic assay in this study was dictated by both assay quality and logistic feasibility. Therefore, we used a commercially available, FDA-approved assay, validated by several research groups, and deployable in a basic clinical lab setting [23–26]. Furthermore, we chose to use both IgG and IgA based on the evidence of distinct but overlapping temporal patterns seen for these antibodies in COVID-19 patients [21, 27, 28]. Thus, both IgG and IgA appear as early as 2 weeks post-symptom onset (PSO), with IgA increasing up to third week PSO and then dropping, while IgG increases until fourth week PSO, remaining detectable up to 8 months PSO [21]. Finally, we chose not to use IgM, since this antibody is more suitable for detecting acute infection, while in convalescent subjects IgA temporally overlaps IgM, resulting in higher positivity rates [27].

We validated our findings in the current cohort by testing pre-pandemic samples and performing virus neutralization assays. Half of the Prior COVID participants exhibited virus neutralization correlating with S-reactive IgG titres. This finding is consistent with the evidence that SARS-CoV-2 neutralization declines rapidly after COVID-19 disease resolution and >40% of convalescent subjects show little neutralization activity [21, 23, 29].

Recent studies indicate that people with prior history of COVID-19 have a stronger response to vaccination compared to COVID-19-naive subjects after one vaccine dose [30–32]. Mass COVID-19 vaccination was launched in Kazakhstan in February 2021, and so far, ~40% of Kazakhstan’s population has received at least one vaccine dose [10]. Considering limited vaccine supply and low vaccination acceptance, our seroprevalence findings therefore could be extended to inform the ongoing vaccination efforts about the existing population-wide anti-SARS-CoV-2 immunity in Kazakhstan. For example, the second dose of COVID-19 immunisation could be reserved for people without prior natural exposure to SARS-CoV-2 but delayed for subjects with prior COVID-19 exposure.

Given the small sample constrained to employees of one, albeit large (employing ~3000 staff) organization, our findings should be seen as preliminary “pilot” data on SARS-CoV-2 prevalence in Kazakhstan. Importantly, our analysis was focused on S-reactive immunoglobulins and with increasing vaccination coverage other SARS-CoV-2 antigens should be incorporated into the future seroprevalence surveys across various demographic groups in the region.

Conclusions

Continuous epidemiologic surveillance of SARS-CoV-2 exposure is critical for understanding the COVID-19 transmission dynamics and for informing ongoing vaccination efforts and other COVID-19 mitigation strategies. Seroprevalence studies could help fill in the current gaps in COVID-19 case reporting in Kazakhstan. However, large scale seroprevalence studies in resource-limited settings may not be feasible due to limited clinical and laboratory infrastructure- a barrier that could be overcome by using alternative approaches such as analysing blood bank samples, assessing data collected by the private laboratory sector (which has recently dramatically expanded in Kazakhstan [19]), and implementing PCR-based wastewater surveillance. Our seroprevalence study for the first time documents an extremely high rate of SARS-CoV-2 exposure in Karaganda, Kazakhstan. Although pilot in nature, these findings are consistent with high SARS-CoV-2 seroprevalence in other regions of Kazakhstan [19, 20, 33], corroborating an overall underascertainment of COVID-19 rates across the country. While narrowing the gaps in country-wide infection surveillance necessitates addressing systemic issues related to governance and healthcare delivery and improving vital registration systems, we hope that our findings can inform the regional pandemic response to facilitate a more effective and equitable distribution of resources, such as vaccines.

Supporting information

Acknowledgments

We thank all the study participants and the COVID-19 vaccination clinic staff. We acknowledge that an earlier draft of this manuscript appeared online as a medRxiv preprint [5].