key: cord-0264505-jmilzi23
authors: Ifedayo M.O. Adetifa, Sophie Uyoga; John N. Gitonga, Daisy Mugo; Mark Otiende, James Nyagwange; Henry K. Karanja, James Tuju; Perpetual Wanjiku, Rashid Aman; Mercy Mwangangi, Patrick Amoth; Kadondi Kasera, Wangari Ng’ang’a; Charles Rombo, Christine Yegon; Khamisi Kithi, Elizabeth Odhiambo; Thomas Rotich, Irene Orgut; Sammy Kihara, Christian Bottomley; Eunice W. Kagucia, Katherine E. Gallagher; Anthony Etyang, Shirine Voller; Teresa Lambe, Daniel Wright; Edwine Barasa, Benjamin Tsofa; Philip Bejon, Lynette I. Ochola-Oyier; Ambrose Agweyu, J. Anthony G. Scott; Warimwe, George M.
title: Temporal trends of SARS-CoV-2 seroprevalence in transfusion blood donors during the first wave of the COVID-19 epidemic in Kenya
date: 2021-02-12
journal: nan
DOI: 10.1101/2021.02.09.21251404
sha: baadc5c1ff92cbff653a1b82300de4d4d479f13f
doc_id: 264505
cord_uid: jmilzi23

nan

Across tropical Africa, numbers of cases and deaths attributable to COVID-19 have been substantially lower than those in Europe and the Americas. This could imply reduced transmission, reduced clinical severity or epidemiological under-ascertainment. The first COVID-19 case in Kenya was identified on 12 th March 2020. Subsequently, there have been two discrete waves of PCR-detected cases separated by a brief nadir in September 2020. At the end of 2020, the governement had recorded 96,458 cases and 1,670 deaths attributable to SARS-CoV-2. When COVID-19 related deaths reached 71, the national anti-SARS-CoV-2 antibody prevalence, estimated in blood donors, was 4.3% (95% confidence interval (CI) 2.9-5.8%) 1 .

Transmission was obviously more widespread than would have been anticipated by reported cases and deaths. In this further study, we examine the dynamics of SARS-CoV-2 seroprevalence among Kenyan blood donors throughout the course of the first epidemic wave.

Human samples.

The Kenya National Blood Transfusion Service (KNBTS) coordinates and screens blood transfusion donor units at 6 regional centres at Eldoret, Embu, Kisumu, Mombasa, Nairobi and Nakuru, though the units are collected across the whole country and each Regional Centre serves between 5-10 of Kenya's 47 Counties. KNBTS guidelines define eligible blood donors as individuals aged 16-65 years, weighing ≥ 50kg, with haemoglobin of 12·5g/dl, a normal blood pressure (systolic 120-129 mmHg and diastolic BP of 80-89 mmHg), a pulse rate of 60-100 beats per minute and without any history of illness in the past 6 months 2 . KNBTS generally relies on voluntary non-remunerated blood donors (VNRD) recruited at public blood drives typically located in high schools, colleges and universities. Since September 2019, because of reduced funding, KNBTS has depended increasingly on family replacement donors (FRD) who provide units of blood in compensation for those received by sick relatives. We obtained anonymized residual samples from consecutive donor units submitted to the 6 regional centres for transfusion compatibility-testing and infection screening, as previously described 1 .

We tested samples for anti-SARS-CoV-2 IgG antibodies using a previously described a previously described ELISA for whole length spike antigen 3 at the KEMRI-Wellcome Trust 

We estimated crude prevalence based on the proportion of samples with OD ratio>2. We also used Bayesian Multi-level Regression with Post-stratification (MRP) 5 to account for differences in the age and sex distribution of blood donors and regional differences in the numbers of samples collected over time. Data on donor residence were specified at County level. For the purposes of analysis and presentation we collapsed the 47 counties into 8 regions based on the previous administrative provinces of Kenya; as data from two regions (Eastern and North Eastern) was relatively sparse we collapsed these to one stratum. The model was also used to adjust for sensitivity (93%) and specificity (99%) of the chosen cut-off value as previously developed 6 . Regional and national estimates were produced by combining model predictions with weights from the 2019 Kenyan census 7 . Two versions of the model were fitted. In the first (Model A), the model included age, sex and region as covariates and was fitted separately to data in three periods (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) . In the second (Model B), the model also included a period effect and was fitted to the samples as a whole. A mathematical description of the models and Rstan code 8 is provided in the appendix. 

From 30 th April to 30 th September 2020, 10,258 blood donor samples were processed at six Kenya National Blood Transfusion Service (KNBTS) regional blood transfusion centres, which serve a countrywide network of satellites and hospitals. We excluded duplicate samples, those from age-ineligible donors and those with missing data, leaving 9,922 samples (Supplementary figure 1).

The blood donor samples were broadly representative of the Kenyan adult population 7 on region of residence and age, although adults aged 55-64 years were under-represented (2.0% v 7.3%, Supplementary table 1) and adults aged 25-34 years were over-represented (39.3% v 27.3%).

Males were also over-represented (80.8%).

Of the 9,922 samples with complete data 3,098 had been reported previously 1 . In total, 928 were positive for anti-SARS-CoV-2 IgG; crude seroprevalence was 9.4% (95% CI, 8.8-9.9%) with little variation by age or sex (Table 1a) . We used Bayesian Multi-level Regression with Poststratification (MRP) to adjust for test sensitivity (93%) and specificity (99%) 6 , smooth trends over time, and account for the differences in age, sex and residence characteristics of the test sample and the Kenyan population 5 .

There was marked variation in seroprevalence over time and place with a generally increasing trend over time. Figure 1 illustrates the crude prevalence and Bayesian model estimates in 10 consecutive periods of approximately 2 weeks each. In Nairobi, Mombasa and the Coastal Region outside Mombasa, there was a steep rise in seroprevalence across the study period. We divided the observations equally into three consecutive periods (Table 1b ). In period 1 (30 April-19 June) the adjusted seroprevalence of SARS-CoV-2 was 5.2% (95% CI 3.7-6.7%); in period 2 (20 June-19 August) it had risen to 9.1% (95% CI 7.2-11.3%); and in period 3 (20 August-30 September) it was maintained at 9.1% (95% CI 7.6-10.8%).

The results illustrate a heterogeneous pattern of transmission across Kenya and suggest that the seroprevalence first began to rise in Mombasa in May and reached a maximum in July; in Nairobi it increased steadily from June onwards; in the Coastal area seroprevalence began to rise in July and turned up sharply in August and September. Unlike Nairobi and Mombasa this area is mostly rural. Other parts of the country showed less of a temporal trend. These field observations . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted February 12, 2021. ; accord closely with epidemic modelling of SARS-CoV-2 across Kenya which integrated early PCR and serological data with mobility trends to describe the transmission pattern nationally 9 .

Although we used a highly specific and validated assay 1,4 , and adjusted for biases inherent in the ELISA test performance, we did not control for antibody waning. Given evidence at both individual 10 and population 11 level that anti-Spike antibodies may decline after an initial immune response, cross-sectional data are likely to underestimate cumulative incidence with increasing error as the epidemic wave declines. Some investigators have adjusted for this effect through modelling 12 but as we do not have a clear description of the waning function for these antibodies in our setting, we have not made such an adjustment. Therefore, the seroprevalence estimates reported here are likely to underestimate cumulative incidence in Kenya.

The study also relies on convenience sampling of blood transfusion donors which is not representative of the adult population at large. We have adjusted for demographic and geographic disparities in our sample set but we are unable to evaluate whether the behaviour of blood donors makes them more or less at risk of infection by SARS-CoV-2. A random population sample would overcome this problem but such studies were difficult to undertake during movement restrictions. The selection bias in KNBTS samples is unlikely to change substantially over time and therefore this survey gives a valid estimation of trends, which inform the public health management of the epidemic.

The results are also consistent with other surveys in Kenya which have illustrated both high seroprevalence in focal populations and marked geographic variation. For example, seroprevalence was 50% among women attending ante-natal care (ANC) in August 2020 in Nairobi but 1.3%, 1.5% and 11.0% among women attending ANC in Kilifi (Coast) in September, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted February 12, 2021. ;

October and November, respectively 13 . Seroprevalence among truck drivers at two sites (in Coast and Western) was 42% in October 2020 14 and seroprevalence among health care workers in between August and November 2020 was 43%, 12% and 11%,in Nairobi, Busia (Western) and

Kilifi (Coast) respectively 15 .

By 1 st September 2020, the first epidemic wave of SARS-CoV-2 in Kenya had declined with a cumulative mortality of 577 COVID-19 deaths 16 . Our large national blood donor serosurvey illustrates that, at the same point, 1 in 10 donors had antibody evidence of infection with SARS-CoV-2; this rises to 1 in 5 in the two major cities in Kenya. The first epidemic wave rose and fell against a background of constant movement restrictions. The seroprevalence estimates suggest that population immunity alone was inadequate to explain this fall and the majority of the population remained susceptible. Nonetheless, they also show that the virus was widely transmitted during the first epidemic wave despite the fact that numbers of cases and deaths attributable to SARS-CoV-2 in Kenya were very low by comparison with similar settings in Europe and the Americas at similar seroprevalence 17,18 .

. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted February 12, 2021. ; https://doi.org/10.1101/2021.02.09.21251404 doi: medRxiv preprint

The data shown in the manuscript are available upon request from the corresponding author. Deidentified data has been published on the Havard dataverse server https://doi.org/10.7910/DVN/FQUNVD . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

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The copyright holder for this preprint this version posted February 12, 2021. ; https://doi.org/10.1101/2021.02.09.21251404 doi: medRxiv preprint proportion seropositi e ate of sample olle tion . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

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Coast 1

Western 2

Regression with Post-stratification (MRP) accounts for differences in the age and sex distribution of blood donors and regional differences in the numbers of samples collected over time. The model also adjusts for sensitivity (93%) and specificity (99%) of the ELISA 15 -24 years