{"paper_id":"279450d8-c810-44e5-8849-bef034c987d9","body_text":"1\n1 Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics \n2 in three Kenyan referral hospitals: repeated cross-sectional \n3 surveys 2020-21 \n4 R. Lucinde 1,*, D. Mugo1, C. Bottomley2, A. Karani1, E. Gardiner1, R Aziza3, J. Gitonga1, H. Karanja1, \n5 J. Nyagwange 1, J. Tuju1, P. Wanjiku1, E. Nzomo4, E. Kamuri5, K. Thuranira5, S. Agunda5, G. Nyutu1, \n6 A. Etyang 1, I. M. O. Adetifa1,2, E. Kagucia1, S. Uyoga1, M. Otiende1, E. Otieno1, L. Ndwiga1 , C. N. \n7 Agoti 1, R. A. Aman6, M. Mwangangi6, P. Amoth6, K. Kasera6, A. Nyaguara1 , W. Ng’ang’a7, L. B. \n8 Ochola 9, E. Namdala10 , O Gaunya10, R Okuku10, E. Barasa1,8, P. Bejon1,8, B. Tsofa1, L. I. Ochola-\n9 Oyier 1, G. M. Warimwe1,8+, A. Agweyu1+, J. A. G. Scott1,2,8+, K. E. Gallagher1,2+.  \n10 * Corresponding author: Ruth Lucinde, Epidemiology & Demography Department, KEMRI-\n11 Wellcome Trust Research Programme CGMR-C, PO Box 230-80108, Kilifi, Kenya. Email: \n12 RLucinde@kemri-wellcome.org \n13 Alternate corresponding author: Katherine Gallagher, Department of Infectious Disease \n14 Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and \n15 Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom. Email: \n16 Katherine.gallagher@lshtm.ac.uk \n17 +Contributed equally\n18 1 KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.\n19 2 Department of Infectious Diseases Epidemiology, London School of Hygiene and Tropical \n20 Medicine, Keppel Street, London, UK.\n21 3 School of Life Sciences and the Zeeman Institute for Systems Biology & Infectious Disease \n22 Epidemiology Research (SBIDER), University of Warwick, Coventry, United Kingdom\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n2\n23 4 Kilifi County Hospital, Ministry of Health, Government of Kenya\n24 5 Kenyatta National Hospital, Ministry of Health, Government of Kenya\n25 6 Ministry of Health, Government of Kenya, Nairobi, Kenya.\n26 7 Presidential Policy and Strategy Unit, The Presidency, Government of Kenya, Nairobi, Kenya\n27 8 Nuffield Department of Medicine, Oxford University, Oxford, UK\n28 9 Institute of Primate Research, Nairobi, Kenya\n29 10 Busia Country Teaching & Referral Hospital, Busia, Kenya\n30\n31 Short title: SARS-CoV-2 seroprevalence at antenatal care clinics in three Kenyan referral \n32 hospitals\n33 Abstract word count: 250/300\n34 Article: 2997/5000\n35 Keywords: SARS-CoV-2, serology, sero-surveillance, ante-natal care\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n3\n36 Abstract \n37 Introduction\n38 The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to \n39 tracking the progress of the pandemic and estimating the extent of population immunity. \n40 Methods\n41 We used residual blood samples from women attending antenatal care services at three hospitals in \n42 Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike \n43 protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component \n44 mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals \n45 with past SARS-CoV-2 infection.\n46 Results\n47 We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By \n48 October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. \n49 Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI \n50 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in \n51 October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in \n52 October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are \n53 underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi.   \n54 Conclusions\n55 There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi \n56 Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional \n57 surveys are now difficult to interpret without the use of models to account for antibody waning. \n58\n59\n60\n61\n62\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n4\n63 Introduction \n64 Globally, as countries are confronted with new waves of SARS-CoV-2 infections and new variants, \n65 WHO recommendations have focused on enhancing population immunity with the available COVID-\n66 19 vaccines(1). In Kenya, as with many lower-middle income countries, COVID-19 vaccine supplies \n67 have been limited(2). Vaccination started in March 2021 and by 31 st October 2021, 3.7 million people \n68 had received their first dose (13.5% of the adult population), 1.6 million had received their second \n69 dose (6% of the adult population), with some geographic heterogeneity. In Nairobi 34% were partially \n70 vaccinated, 18% were fully vaccinated, compared to Busia where 8% were partially vaccinated, 3% \n71 fully vaccinated, and Kilifi where 5% were partially vaccinated, 2% fully vaccinated(3).\n72 As vaccine coverage and vaccine-induced immunity is still considered to be low in Kenya, it remains \n73 important to track the potential protection conferred by natural infection. By the 31 st October 2021, \n74 Kenya had experienced four waves of infections, and reported a total of 253,310 confirmed cases and \n75 5281 deaths. However, with just 3.9% of the population over 65 years of age(4), the proportion of \n76 infections that have been asymptomatic is likely to be very high(5). Additionally, limited access to \n77 tests and low uptake of testing makes it likely that a substantial proportion of cases have remained \n78 undetected. Measuring the prevalence of antibodies to SARS-CoV-2 is an alternative way to estimate \n79 the cumulative incidence of infection. A number of serological assays have been developed and \n80 perform well with high sensitivity and specificity(6-9). We have shown that 5.2% of blood donors in \n81 Kenya had SARS-CoV-2 antibodies in June 2020 and this had risen to 9.1% in September 2020 and \n82 48.5% by March 2021(10-12).  However, it is unclear whether blood donors are representative of the \n83 population.\n84 In the context of a pandemic, sentinel public health surveillance using residual aliquots of routinely \n85 collected blood samples has the potential to overcome participation bias. For example, sero-\n86 surveillance for HIV among women attending antenatal care was used to track the progress of the \n87 HIV pandemic and showed prevalence estimates that were similar to population samples from the \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n5\n88 same areas(13, 14). It remains unclear whether pregnancy alters susceptibility to SARS-CoV-2 \n89 infection(15); however, a large systematic review has found no difference in risk of becoming \n90 symptomatic when comparing pregnant women with confirmed SARS-CoV-2 infection in women of \n91 the same age(16, 17). \n92 In Kenya, in 2014, 50% of women had had at least one pregnancy or were pregnant by 20 years of age \n93 and the coverage of at least one antenatal care visit was 96%(18). Residual blood samples from \n94 mothers visiting antenatal care for the first time may therefore represent a relatively unbiased sample \n95 of young women, and an alternative sentinel surveillance population to blood donors. Testing an \n96 aliquot of blood for antibodies to SARS-CoV-2 is feasible as a venous blood sample (5ml) is already \n97 taken to screen mothers for malaria, HIV and syphilis at their first ANC visit. We aimed to determine \n98 the prevalence of antibodies against SARS-CoV-2 in mothers attending ANC at three referral \n99 hospitals in Kenya. \n100 Methods\n101 Setting\n102 In a collaboration between the Kenyan Ministry of Health (MOH) and KEMRI-Wellcome Trust \n103 Research Programme (KWTRP), three referral hospitals were engaged. Kenyatta National Hospital \n104 (KNH) is the national referral tertiary hospital located in Nairobi, the country’s capital city, \n105 approximately 3km from the central business district. The population of Nairobi city was 4,397,073 in \n106 2019(4). Busia Country Teaching & Referral Hospital (BCTRH) serves Busia County, an area of \n107 1628 km 2, with a population of 893,681 (548/ km2). Kilifi County Hospital (KCH) is the county \n108 referral hospital in Kilifi Town. Kilifi county covers an area of 12,000 km 2, with a predominantly \n109 rural population of 1.4 million (116/km 2)(4). \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n6\n110 Study population\n111 All women attending ANC for the first time, who provided a routine blood sample at their clinic visit, \n112 were included in the study. Women who did not provide a sample at their first antenatal care visit, or \n113 women attending their second or subsequent ANC visit, were excluded. \n114 Sample collection and processing\n115 In Kenya, a 5ml blood sample is routinely collected at the first ANC visit. After testing for malaria, \n116 syphilis and HIV in the hospital laboratory, the residual volume is usually discarded. In this study, all \n117 residual samples were set aside and collected daily for SARS-CoV-2 sero-surveillance. Where \n118 possible, the following data were collected from hospital records and linked to the residual sample \n119 identity number: date of sample, age, sub-county of residence, trimester of pregnancy, presence or \n120 absence of COVID19-like symptoms in the last month and COVID-19 vaccination status ascertained \n121 via verbal report confirmed via SMS or certificate. No personal identifiers were collected. All samples \n122 were tested at the KWTRP laboratories for IgG to SARS-CoV-2 whole spike protein using an \n123 adaptation of the Krammer Enzyme Linked Immunosorbent Assay (ELISA)(6). Validation of this \n124 assay is described in detail elsewhere(10). Results were expressed as the ratio of test OD to the OD of \n125 the plate negative control; samples with OD ratios greater than two were considered positive for \n126 SARS-CoV-2 IgG. Sensitivity, estimated in 174 PCR positive Kenyan adults and a panel of 5 sera \n127 from the National Institute of Biological Standards in the UK was 92.7% (95% CI 87.9-96.1%); \n128 specificity, estimated in 910 serum samples from Kilifi drawn in 2018 was 99.0% (95% CI 98.1-\n129 99.5%)(10).\n130 Analysis\n131 We estimated the proportion of samples seropositive for IgG to SARS-CoV-2. Sampling at least 135 \n132 women per month from each hospital would provide estimates of seroprevalence in the range 3-25% \n133 with a precision of 3-7%. Bayesian modelling was used to adjust seroprevalence estimates for the \n134 sensitivity and specificity of the assay. Non-informative priors were used for each parameter \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n7\n135 (sensitivity, specificity and proportion true positive) and the models were fitted using the RStan \n136 software package(19) (see supplementary files for code). Sub-county population densities were \n137 extracted from the Kenya National Bureau of Statistics’ database(4). \n138 To account for the effects of waning IgG in repeated cross-sectional samples, we fitted a two-\n139 component mixture model to the log 2 OD ratios in unvaccinated individuals. In this model, we \n140 assumed that antibody levels follow a normal distribution in previously uninfected individuals and a \n141 skew-normal in previously infected individuals. To fit the model, we fixed the standard deviation of \n142 the negative component at the value observed in pre-COVID 19 samples. The remaining parameters \n143 were estimated using RStan. Details of the priors used in the estimation have been described \n144 elsewhere(20). \n145 Patient and Public Involvement \n146 The study was conducted as anonymous public health surveillance at the request of the Kenyan MOH, \n147 in response to the COVID-19 pandemic. The study directly addressed the needs of the MOH by \n148 providing some information on the extent of the spread of SARS-CoV-2 pandemic within Kenya. The \n149 public were not involved in the conceptualisation or implementation of this study. The need for \n150 individual informed consent from the women whose samples were studied was waived, the protocol \n151 was approved by the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research \n152 Institute (Protocol SSC 4085), the Kenyatta National Hospital – University of Nairobi Ethics \n153 Review Committee (Protocol P327/06/2020) and the Busia & Kilifi County health management \n154 teams. \n155 Results \n156 Crude and adjusted seroprevalence across time and location \n157 In Nairobi, samples were collected in three rounds: round 1, median date 11 th August 2020, round 2, \n158 median date 22 February 2021, and round 3, median date 30 September 2021. In these time periods, \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n8\n159 706 women (93%) provided a sample (Supplementary Figure 1). Women were aged between 17 and \n160 45 years (mean 31 years); 275 (40%) attended their first antenatal care visit during their third \n161 trimester of pregnancy, although this differed significantly between the rounds: 62% in August 2020, \n162 28% in February 2021 and 35% in September 2021 (p<0.001; Supplementary Table 1). A total of 632 \n163 (90%) reported residence in 16 different sub-counties of Nairobi, 267 (42%) of mothers were resident \n164 in Embakasi North, East or West sub-counties, and 110 (17%) were resident in Dagoretti North or \n165 South sub-counties. The proportion of participants living in high vs. low population density sub-\n166 counties did not differ by round (supplementary Table 1). Among women who had data on symptoms \n167 during the preceding month, 7% reported symptoms in the first two rounds, this significantly differed \n168 from the third round where 43% reported symptoms, coinciding with the end of the cold season (June-\n169 September). Symptoms were not associated with seropositivity, controlling for age (data not shown).  \n170 In Nairobi, seroprevalence, adjusted for the sensitivity and specificity of the ELISA, was 50% in \n171 August 2020, 32% in February 2021 and 85% in September 2021 (Table 1). In October 2021, 12.7% \n172 of women were vaccinated with at least one dose of COVID-19 vaccine, seroprevalence among the \n173 unvaccinated was 82%. \n174 In Busia, samples were collected in 2 rounds: round 1, median date 3 rd May, and round 2, median date \n175 5 th October. In this time period a total of 567 first ANC visits were conducted; 567 (100%) provided a \n176 sample (Supplementary Figure 1). Women were aged between 14 and 44 (mean age 27 years). Most \n177 women (66%) attended their first ANC in their second trimester, although this differed by round (73% \n178 in May and 60% in October (p=0.007; Supplementary table 1). In May, 40% of women reported \n179 symptoms in the last month, which differed significantly from October 2021 where 56% reported \n180 symptoms. Symptoms were not associated with seropositivity, controlling for age (data not shown). \n181 Adjusted seroprevalence in Busia increased from 31% in May 2021 to 71% in October 2021 (Table \n182 2). Just 6 (2%) of women were vaccinated in October 2021, and seroprevalence remained 71% among \n183 the unvaccinated. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n9\n184 Table 1. Seroprevalence of IgG to SARS-CoV-2 among mothers attending antenatal care in Kenyatta National Hospital (KNH), Nairobi\n30th July – 25th August 2020 27th Jan- 11th March 2021 7th September-19th October 2021\nKNH Seroprevalence\nAdjusted \nseroprevalence Seroprevalence\nAdjusted \nseroprevalence Seroprevalence\nAdjusted \nseroprevalence\nNairobi n / N % % 95% CI n / N % % 95% CI n / N % % 95% CI\nAll 91 / 196 46.4 49.9 42.1-58.2 80 / 265 30.2 32.1 26.2-38.4 193 / 245 78.8 84.9 78.3-91.5\nAge\n17-29 years 39 / 93 41.9 44.9 33.8-56.9 28 / 101 27.7 29.6 20.7-39.2 82 / 101 81.2 87.2 77.9-95.7\n30-45 years 44 / 90 48.9 52.5 41.1-63.7 47 / 141 33.3 35.7 27.3-44.3 107 / 139 77.0 83.0 74.0-91.7\nTrimester\nFirst 7 / 17 41.2 44.9 21.2-70.1 27 / 83 32.5 35.0 24.1-46.4 48 / 60 80.0 85.5 73.0-96.1\nSecond 21 / 53 39.6 42.9 29.8-57.4 34 / 106 32.1 34.5 25.1-44.3 77 / 96 80.2 86.1 76.0-95.5\nThird 58 / 114 50.9 54.7 44.6-64.6 18 / 75 24.0 25.8 16.4-37.0 64 / 84 76.2 81.6 71.2-91.7\nAny symptoms in last month*\nYes 7 / 12 58.3 61.2 33.3-86.1 3 / 18 16.7 20.8 5.5-42.2 80 / 106 75.5 81.2 71.3-90.5\nNo 78 / 172 45.3 48.7 40.2-57.5 77 / 247 31.2 33.3 26.9-40.0 113 / 139 81.3 87.5 79.3-95.1\nPopulation density of sub-county of residence \n<20000/km2 44 / 97 45.4 48.8 38.1-59.4 29 / 104 27.9 29.8 20.6-39.8 86 / 102 84.3 90.6 82.2-97.9\n20-81000/km2 39 / 79 49.4 53.1 41.0-65.7 40 / 124 32.3 34.6 25.9-44.1 85 / 115 73.9 79.5 70.3-88.5\nCOVID-19 vaccine status†\nVaccinated - - - - - - - - 30 / 31 96.8 96.1 86.2-99.9\nUnvaccinated - - - - - - - - 163 / 214 76.2 82.1 75.1-89.2\n185\n186 * Women were asked about the full list of COVID-19 symptoms as per the MOH COVID-19 screening form i.e. fever/ chills, general weakness, cough, sore throat, runny \n187 nose, shortness of breath, diarrhoea, nausea/ vomiting, headache, irritability/ confusion, pain (muscular/ chest/ abdominal/ joint). \n188 † Vaccination status (at least one dose) was not available for the first two rounds of data collection, vaccination began in Kenya in March 2021 and at first targeted specific \n189 groups only, we assume that the vaccine coverage among women attended ANC between 27th Jan-11th March was 0%. \n190 Variations in seroprevalence by any of the explanatory variables were not statistically significant in any time period when tested with chi2 test. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n10\n191 Table 2. Seroprevalence of IgG to SARS-CoV-2 among mothers attending antenatal care in Busia County Teaching & Referral Hospital (BCTRH), \n192 Busia \n193\n15th April - 21 May 2021 20 September – 22 October 2021\nSeroprevalence Adjusted \nseroprevalence\nSeroprevalence Adjusted \nseroprevalence\n BCTRH, Busia \nn / N % % 95% CI n / N % % 95% CI\nAll 78 / 270 28.9 30.6 24.7-37.1 195 / 297 65.7 70.7 64.1-77.4\nAge\n17-29 years 3 / 14 21.4 26.0 6.5-51.4 128 / 203 63.1 67.8 59.8-76.0\n30-45 years 2 / 7 28.6 35.3 7.4-71.3 58 / 84 69.0 74.2 62.7-85.4\nTrimester\nFirst 18 / 51 35.3 38.2 25.1-52.6 50 / 78 64.1 68.7 56.6-80.1\nSecond 50 / 192 26.0 27.7 21.3-34.7 114 / 175 65.1 70.1 61.8-78.4\nThird 7 / 22 31.8 35.3 17.3-55.8 26 / 39 66.7 71.2 54.3-86.4\nAny symptoms in last month*\nYes 37 / 109 33.9 36.4 26.6-46.2 107 / 164 65.2 70.1 61.6-78.8\nNo 41 / 161 25.5 27.0 19.6-35.1 84 / 129 65.1 70.0 60.4-79.3\nCOVID-19 vaccination status†\nVaccinated - - - - 4 / 6 66.7 67.0 29.8-96.1\nUnvaccinated - - - - 188 / 288 65.3 70.5 63.8-77.3\n194 1DOB was only available for 22 women in the first round of data collection \n195 * Women were asked about the full list of COVID-19 symptoms as per the MOH COVID-19 screening form i.e. fever/ chills, general weakness, cough, sore throat, runny \n196 nose, shortness of breath, diarrhoea, nausea/ vomiting, headache, irritability/ confusion, pain (muscular/ chest/ abdominal/ joint). \n197 † Vaccination status (at least one dose) was not available for the first two rounds of data collection, vaccination began in Kenya in March 2021 and at first targeted specific \n198 groups only, we assume that the vaccine coverage among women attended ANC between 27th Jan-11th March was 0%. \n199 Variations in seroprevalence by any of the explanatory variables were not statistically significant in any time period when tested with chi2 test. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n11\n200 In Kilifi, 1707 samples were collected between the 18 th September 2020 and 22nd October 2021, \n201 collection was continuous apart from during the healthcare worker strike (December 2020 to February \n202 2021). No data were available on age, trimester, location, symptoms or COVID-19 vaccination status. \n203 Adjusted seroprevalence increased over the period of sample collection from 1% in September 2020 \n204 to 63% in October 2021 (p=0.0001, Chi sq test for trend; Table 3). \n205 Table 3: Seroprevalence of IgG to SARS-CoV-2 among mothers attending antenatal care in \n206 Kilifi County Hospital (KCH), over time\nKCH, Kilifi1 Seroprevalence Adjusted seroprevalence\nn / N % % 95% CI\nMonth Sept-Oct 2020 3 / 265 1.1 0.9 0.0-2.7\nNov-Dec 2020 32 / 236 13.6 14.0 9.4-19.5\nMar-Apr 2021 55 / 260 21.2 22.2 16.7-28.1\nMay-Jun 2021 104 / 382 27.2 28.9 23.9-34.4\nJul-Aug 2021 148 / 260 56.9 61.2 54.4-68.4\nSept-Oct 2021 178 / 305 58.4 62.7 56.2-69.1\n207 1 No age, trimester or symptom data were available from the ANC records at KCH. Months were combined into \n208 2-month batches due to low numbers \n209\n210 Mixture model results \n211 When two distinct distributions were fitted to the data—corresponding to antibody levels in \n212 previously infected and previously uninfected individuals—there were substantial overlaps in the \n213 distributions, especially at low seroprevalences (Figure 1). As seroprevalence estimates increased, the \n214 distributions became more distinct. The mixture model produced estimates of cumulative incidence \n215 that were consistently higher than those of the threshold analysis except for the final round in Nairobi, \n216 where the results of both analyses were the same (85%) and the distributions hardly overlapped (85%; \n217 Figure 2). Median OD ratios among the unvaccinated, seropositive individuals increase over time in \n218 all three areas, potentially indicating natural boosting through re-infections (Figure 1, Supplementary \n219 Table 2). \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n12\n220 Figure 1. Mixture distributions fitted to anti-spike IgG antibody data collected in Kilifi (KCH), \n221 Busia and Nairobi (KNH) . The red distribution represents predicted responses in individuals \n222 previously infected with SARS-CoV-2 and the blue distribution represents predicted responses in \n223 previously uninfected individuals.\n224 Figure 2. Adjusted and modelled estimates of the cumulative incidence of SARS-CoV-2 \n225 infection.  Estimates are shown with 95% credible intervals.\n226 Discussion \n227 Surveillance for IgG antibodies to SARS-CoV-2 among mothers attending ANC services in three \n228 county referral hospitals in Kenya has revealed evidence of a substantial amount of prior infection by \n229 October 2021. Seroprevalence is currently highest in Nairobi, then Busia, then Kilifi, correlating with \n230 the counties’ population densities. \n231 In Nairobi, in August 2020, just after the peak of the first wave of SARS-CoV-2 infections, mixture \n232 modelling, which attempts to account for the wide range of OD ratios among those exposed to the \n233 virus better than the simple threshold analysis(20), indicates a cumulative incidence of 75%, just 4 \n234 months after the start of the pandemic. At the same timepoint, 6,727 PCR-confirmed infections had \n235 been registered across the city (<1% of the County’s population; Supplementary Figure 2). In March \n236 2021, a year after the pandemic began, seroprevalence was lower, 32%. This second group of women \n237 reported residing in the same sub-counties and were on average the same age (supplementary table 1). \n238 The high levels of transmission of the virus in these locations early in the pandemic may have meant \n239 some of these women had been infected at some point in the last year, but had since seroreverted. \n240 Data on the rate of seroreversion differs with the assay used(21) and the severity of the initial \n241 infection(22); approximately 9-12% with mild symptoms may sero-revert 4-6 months post-\n242 infection(23, 24). Additionally, such high seroprevalence earlier in the year may have reduced the \n243 number susceptible and dampened transmission within the same communities by March 2021. \n244 Modelling indicates the first wave could have predominantly affected communities of low-income \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n13\n245 earners, more likely to use public hospitals, such as KNH, with the later waves affecting a group \n246 including higher-income, private healthcare users(25). In October 2021, seroprevalence in a third \n247 group of women was 85%, 76% among those unvaccinated. These women were very similar in age, \n248 residence location and trimester to the second group. The survey was conducted just after the fourth \n249 wave of cases in Kenya, natural boosting could have occurred in these communities if residents \n250 encountered the virus repeatedly(26). This is supported by the higher median OD ratios among \n251 unvaccinated, seropositive individuals in the third round compared to both of the previous rounds. \n252 The proportion vaccinated with at least one dose (13%) is lower than the average for Nairobi adults at \n253 this time of 34%(3), although nationally only 13.5% of the population were vaccinated with one dose \n254 at this time point. \n255 In Busia, seroprevalence in May 2021 was 31% using the threshold analysis, and 51% using the \n256 mixture model analysis. The two distributions of OD ratios had a substantial amount of overlap \n257 leading to uncertainty in the estimate from mixture modelling at this time point. Threshold \n258 seroprevalence increased to 71% in October 2021. By 21 st October 2021 only 2% of our study \n259 population were vaccinated with at least one dose, lower than the nationally reported coverage of 8% \n260 in Busia(3). Western Kenya was affected in the fourth wave of the pandemic in July-September 2021 \n261 and the seroprevalence represents a substantial amount of natural infection.  \n262 Seroprevalence steadily increased in Kilifi during the sampling timeframe to 63% (using the threshold \n263 analysis) or 72% (using the mixture model analysis) in October 2021. The slower increase in \n264 seroprevalence in Kilifi compared to Nairobi is consistent with modelling suggesting that the initial \n265 wave of the COVID-19 pandemic was concentrated in urban centres, with subsequent spread \n266 increasingly affecting rural areas(25); Kilifi County reported a marked increase in the number of \n267 infections in December 2020 (Supplementary Figure 2). \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n14\n268 A strength of this analysis is the use of a rigorously validated serological assay, using locally relevant \n269 control populations and reference panels from the National Institute for Biological Standards and \n270 Control (NIBSC) in the UK(27). The threshold used to define seropositivity was chosen to prioritise \n271 specificity over sensitivity, i.e. to minimize the number of false positives. The very low crude \n272 seroprevalence (0/82; 0%) in the first month of samples from Kilifi adds confirmation that the \n273 specificity of this assay is very high. \n274 Although sero-surveillance among pregnant women has been used as a proxy for population-based \n275 SARS-CoV-2 surveillance in high income countries(28-34), the representativeness of the sample in \n276 Kenya is unknown. A national survey in 2014 indicated 18% of the population utilised public \n277 hospitals at their last visit to outpatient services (a further 40% utilized public health centres or \n278 dispensaries). Utilisation of public health services was correlated with lower education levels(35). \n279 The seroprevalence estimates from women attending ANC differ from the seroprevalence estimates \n280 available from blood donor samples.  The Nairobi seroprevalence of 50% in August is substantially \n281 higher than the 10% seroprevalence reported among blood donors in the same county in June-August \n282 2020(10), however in March 2021, 32% seroprevalence in ANC was lower than the estimated 62% \n283 seroprevalence in blood donors(12). The majority of expectant mothers attending ANC in KNH \n284 consistently came from 5 sub-counties close to the hospital, which are densely populated with low-\n285 income earners(4). Blood transfusion donors are likely to be more heterogenous and widely \n286 distributed across Nairobi including areas of lower population density and greater affluence. The \n287 estimates from ANC in Kilifi in September 2020 (1%) and April 2021 (22%) were lower than the \n288 14.1% seroprevalence reported among blood donors from Coastal Counties in September 2020 and \n289 the 43% seroprevalence among blood donors in Jan-March 2021(10-12). Blood donations come from \n290 across the county including urban centres such as Malindi, whereas women attending ANC in Kilifi \n291 represent a less heterogenous semi-urban group. It is clear that viral transmission has been \n292 heterogenous in terms of geography and socioeconomic status, seroprevalence estimates from \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n15\n293 multiple different sentinel populations provide more reliable indicators of the development of the \n294 pandemic than any one estimate alone.\n295 The impact of pregnancy on susceptibility to SARS-CoV-2 infection is unclear(15); however, \n296 comparisons of infected pregnant women with non-pregnant women of the same age suggests that a \n297 similar proportion of infections become symptomatic(16). This would suggest a similar proportion of \n298 pregnant and non-pregnant women mount a protective antibody response and seroprevalence \n299 estimates are generalisable to non-pregnant women of the same age. In a comparison of samples from \n300 blood donors and ANC in Australia, the two sample sets estimated seroprevalence within 0.1% of \n301 each other, although overall prevalence was very low(36). Additionally, seroprevalence in blood \n302 donors in Kenya did not differ by sex(10), suggesting that these results from pregnant women may be \n303 generalisable to men between 17-45 years of age, residing in the same areas.\n304 Our analysis is constrained by the nature of the anonymised surveillance data available. Data on age, \n305 trimester and location for the women in Kilifi would have allowed more valid comparisons with data \n306 from other sources. It is difficult to assess how comparable the different rounds from the same \n307 location are, without more data. As discussed, we lack local data on the rate of antibody waning, \n308 which is important to estimate cumulative incidence of infection from snapshot seroprevalence \n309 estimates(37, 38). This is especially important in populations, like those reported here, where ongoing \n310 transmission may cause ‘natural boosting’(37, 39, 40).  \n311 Conclusions \n312 This seroprevalence study of women attending ANC clinics suggests there has been substantial, \n313 unobserved transmission of SARS-CoV-2 within communities in Nairobi, Busia and Kilifi Counties. \n314 However, it is becoming difficult to interpret the results of cross-sectional seroprevalence studies due \n315 to the length of the pandemic(41). To attempt to account, to some extent, for antibody waning, we \n316 have used mixture modelling, this suggests that 85% of the population using a public hospital in \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n16\n317 Nairobi have been previously infected with SARS-COV-2. At least in the short-term, asymptomatic \n318 infection is protective(26) and these seroprevalence estimates should be taken into account when \n319 estimating population level immunity. Increases in antibody concentration over time implies an \n320 increasing level of population protection that may be attributable to reinfections. \n321 Acknowledgements\n322 We thank the Kenyatta National Hospital, Busia Country Teaching & Referral Hospital and Kilifi \n323 County Hospital employees who collected the samples during routine ANC visits and the women \n324 themselves for providing samples for routine health screening. We thank Rebeccah Ayako, Evalyne \n325 Akinyi and Cedrick Shikoli at the Institute of Primate Research for processing the ANC samples from \n326 KNH. We thank F. Krammer for providing the plasmids used to generate the spike protein used in this \n327 work. Development of SARS-CoV-2 reagents was partially supported by the NIAID Centres of \n328 Excellence for Influenza Research and Surveillance (CEIRS) contract HHSN272201400008C. The \n329 COVID-19 convalescent plasma panel (NIBSC 20/118) and research reagent for SARS-CoV-2 Ab \n330 (NIBSC 20/130) were obtained from the NIBSC, UK. We also thank the WHO SOLIDARITY II \n331 network for sharing of protocols and for facilitating the development and distribution of control \n332 reagents. This paper has been published with the permission of the director, Kenya Medical Research \n333 Institute.\n334 For the purpose of Open Access, the author has applied a CC-BY public copyright licence to any \n335 author accepted manuscript version arising from this submission.\n336 Funding \n337 This project was funded by the Wellcome Trust (grants 220991/Z/20/Z and 203077/Z/16/Z), the Bill \n338 and Melinda Gates Foundation (INV-017547), and the Foreign Commonwealth and Development \n339 Office (FCDO) through the East Africa Research Fund (EARF/ITT/039) and is part of an integrated \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n17\n340 programme of SARS-CoV-2 sero-surveillance in Kenya led by KEMRI Wellcome Trust Research \n341 Programme.\n342 A.A. is funded by a DFID/MRC/NIHR/Wellcome Trust Joint Global Health Trials Award \n343 (MR/R006083/1), J.A.G.S. is funded by a Wellcome Trust Senior Research Fellowship (214320) and \n344 the NIHR Health Protection Research Unit in Immunisation, I.M.O.A. is funded by the United \n345 Kingdom’s Medical Research Council and Department For International Development through an \n346 African Research Leader Fellowship (MR/S005293/1) and by the NIHR-MPRU at UCL (grant \n347 2268427 LSHTM). G.M.W. is supported by a fellowship from the Oak Foundation. C.N.A. is funded \n348 by the DELTAS Africa Initiative [DEL-15-003], and the Foreign, Commonwealth and Development \n349 Office and Wellcome (220985/Z/20/Z). S.U. is funded by DELTAS Africa Initiative [DEL-15-003], \n350 L.I.O.-O. is funded by a Wellcome Trust Intermediate Fellowship (107568/Z/15/Z). R.A is funded by \n351 National Institute for Health Research (NIHR) (project reference 17/63/82) using UK aid from the UK \n352 Government to support global health research.\n353 The views expressed in this publication are those of the authors and not necessarily those of the \n354 funding agencies\n355 Conflict of Interest\n356 All authors: No reported conflicts.\n357 Author contributions \n358 Conceptualisation: A. Agweyu, J. A. G. Scott, G. M. Warimwe, K. E. Gallagher\n359 Data curation:  G. Nyutu\n360 Formal statistical analysis: K Gallagher, Christian Bottomley \n361 Funding acquisition: A Agweyu J. A. G. Scott, G. M. Warimwe\n362 Investigation (Data collection and Lab): R. Lucinde, D. Mugo, A. Karani, E. Gardiner, J. Gitonga, H. \n363 Karanja, J. Nyagwange, J. Tuju, P. Wanjiku, E. Nzomo, E. Kamuri, K. Thuranira, S. Agunda, L. B. \n364 Ochola, E. Namdala, O Gaunya, R Okuku\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n18\n365 Methodology: K. Gallagher, C Bottomley \n366 Project Administration: R Lucinde, \n367 Supervision: .A Agweyu, J. A. G. Scott, G. M. Warimwe, K. E. Gallagher\n368 Validation: K Gallagher, G Warimwe \n369 Vizualisation: K Gallagher, R Aziza, C Bottomley \n370 Original draft preparation: R Lucinde, K Gallagher \n371 Review and Editing: R. Lucinde, D. Mugo, C. Bottomley, A. Karani, E. Gardiner, R Aziza, J. \n372 Gitonga, H. Karanja, J. Nyagwange, J. Tuju, P. Wanjiku, E. Nzomo, E. Kamuri, K. Thuranira, S. \n373 Agunda, G. Nyutu, A. Etyang, I. M. O. Adetifa, E. Kagucia, S. Uyoga, M. Otiende, E. Otieno, L. \n374 Ndwiga, C. N. Agoti, R. A. Aman, M. Mwangangi, P. Amoth, K. Kasera, A. Nyaguara , W. Ng’ang’a, \n375 L. B. Ochola, E. Namdala, O Gaunya, R Okuku, E. Barasa, P. Bejon, B. Tsofa, L. I. Ochola-Oyier, G. \n376 M. Warimwe, A. Agweyu, J. A. G. Scott, K. E. Gallagher .  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n19\n377 References\n378 1. World Health Organization. COVID-19 vaccines \n379 https://www.who.int/emergencies/diseases/novel-coronavirus-2019/covid-19-vaccines [Accessed 08 \n380 Dec 2021] 2021 [\n381 2. 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Improving \n489 SARS-CoV-2 cumulative incidence estimation through mixture modelling of antibody levels. \n490 medRxiv. 2021:2021.04.09.21254250.\n491\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted March 6, 2022. ; https://doi.org/10.1101/2022.03.03.22271860doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}