Seroprevalence of Dengue, Chikungunya and Rift Valley fever virus IgG antibodies in Kenyan blood donors

Seroprevalence of Dengue, Chikungunya and Rift Valley fever virus IgG antibodies in Kenyan blood donors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Seroprevalence of Dengue, Chikungunya and Rift Valley fever virus IgG antibodies in Kenyan blood donors James Nyagwange This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9294911/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dengue virus (DENV), chikungunya virus (CHIKV) and Rift Valley fever virus (RVFV) continue to cause recurrent outbreaks in East Africa, yet contemporary national seroprevalence data remain absent. We used a new in-house multiplex immunoassay (arbo-plex MIA) to estimate the national burden of arboviral exposure across Kenya. We validated the arbo-plex MIA against Foci Reduction Neutralization Test (FRNT) and commercial IgG ELISAs. Subsequently, we tested 11,124 blood donor samples, collected nationwide from individuals aged 15-64 years in 2020 /2021, for anti-IgG antibodies to DENV, CHIKV and RVFV. Seroprevalence estimates were stratified by age, sex and region. In 2020/21, crude national seroprevalence was 20.9% for DENV, 24% for CHIKV and 4.2% for RVFV while Bayesian population-weighted and test-performance-adjusted national seroprevalence was 7.6% for DENV, 18.4% for CHIKV and 3.1% for RVFV. Seropositivity increased with age for all viruses. The highest seroprevalence for DENV (58.2%) and CHIKV (32.8%) occurred in the Coast region, while RVFV burden was greatest in North-Eastern (5.3%). The arbo-plex MIA proved to be a sensitive, specific and operationally efficient platform for integrated arbovirus surveillance. Overall, the findings suggest that approximately one in five people in Kenya had evidence of prior CHIKV exposure by 2020/21, highlighting widespread transmission and a potentially substantial but under-recognized public health burden that warrants urgent attention. In contrast, DENV and RVFV appear more geographically focal, with persistent but clustered transmission patterns, suggesting that targeted surveillance and region-specific control strategies may be more appropriate for their containment. Health sciences/Medical research/Epidemiology Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Introduction The global impact of Aedes borne arboviruses has increased steadily, driven by their rapid geographic expansion, the emergence of new variants, and complications following sequential exposure by viruses such as DENV and Zika causing enhanced disease severity [1, 2]. This spread is fueled by climate change, urbanization, increased connectivity and invasive vector species [1, 3]. In Kenya, the arboviruses responsible for the most significant human infections and public health impact are DENV, CHIKV, and RVFV. DENV was first reported in Malindi in 1982 [4] and has since caused multiple outbreaks, including in Mombasa (2013, 2014, 2017, 2019, 2021), Mandera (2011), Wajir (2017), and Lamu (2021), all in the North East and South East of Kenya [5-8]. CHIKV was first reported in Lamu in 2004, with subsequent outbreaks in Mombasa (2004, 2018, 2022) and Mandera (2016) [9, 10]. RVFV, first identified in 1930 near Lake Naivasha [11], caused major outbreaks in 1997–1998, 2006–2007 [12, 13], with a more widespread recent outbreak in 2018 affecting Garissa, Kajiado, Kitui, Marsabit, Tana River, and Wajir, where the human case fatality ratio was approximately 23% [14]. Despite these outbreaks, national estimates of the population burden are lacking; the only nationally representative serosurvey was conducted in 2007 [15], and sero-studies done in 2019 were limited to specific regions of the country [16, 17]. Surveillance of these viruses by clinical, serological or molecular methods is important for planning and implementation of control measures. However, these viruses present with similar clinical symptoms complicating the use of presumed diagnosis in surveillance figures [18]. Molecular methods targeting nucleic acid amplification are useful, but patients are only positive for a short period of time during the viremic phase [1, 18]. Serological methods target persistent antibodies produced following an infection, thus allowing surveillance of the viruses’ for several months or years after their initial infection [18]. Serological data are useful in informing public health responses, such as targeted vaccine deployment, by identifying spatial, demographic, and temporal heterogeneity in population immunity and susceptibility. Seroprevalence can also be useful in approximating cumulative incidence at the onset of a new epidemic such as COVID-19, and, in the absence of robust clinical surveillance, can be useful in inferring the transmission of the virus in a population [19]. Several serological methods have been employed in the surveillance of chikungunya (CHIKV), dengue (DENV) and Rift Valley Fever virus (RVFV). Among them are standard enzyme-linked immunosorbent assays (ELISA) used to measure antibodies against target protein antigens [20-22]. Although commercially available, they are expensive and time-consuming. ELISAs allow analysis of only a single antigen at a time and cumulatively require a large sample. Furthermore cross-reactivity among arboviruses compromises diagnosis and interpretation of data acquired using these assays [23]. Neutralization assays such as Foci Reduction Neutralization Test (FRNT) are considered the gold standard in the serology of arboviruses [24-26]. However, neutralization assays are laborious and time consuming and not feasible for large serosurveillance studies. In this study, we have developed a multiplex bead-based immunoassay (MIA), arbo-plex for simultaneous detection of CHIKV, DENV and RVFV, IgG antibodies based on a Luminex® platform (Luminex Corp.), validated it with a wide range of samples and used FRNT as gold standard to define the sensitivity and specificity of the developed arbo-plex MIA. This has allowed us to take advantage of the arbo-plex to simultaneously assay the three pathogens, thereby saving on cost, time and sample volumes while detecting antibodies specific to the three viruses based on the FRNT gold standard assay. To further validate the in-house arbo-plex MIA, we compared it to widely used commercial ELISAs. We then used nationally representative blood donor samples to estimate seroprevalence of DENV, CHIKV and RVFV IgG antibodies in Kenya. Results Assay validation Using FRNT as the gold standard, we selected cut-offs for the arbo-plex MIA where most of the samples were classified correctly. The sensitivity and specificity of the in-house arbo-plex MIA was 91.2% (52 of 57; 95% CI 80.1–96.4%) and 85.5% (65 of 76; 95% CI 75.4–91.9%) respectively, for DENV with 87.9% of the samples correctly classified, 94.2% (81 of 86; 95% CI 86.6–97.6%) and 91.8% (56 of 61; 95% CI 81.4–96.6%) for CHIKV with 93.2% of the samples correctly classified and 100% (10 of 10; 95% CI 69.1–100%) and 95.2% (140 of 147; 95% CI 90.2–97.7%) for RVFV with 95.3% of the samples correctly classified. The Receiver Operating Characteristic curves for DENV, CHIKV and RVFV had AUC of 0.91, 0.96 and 0.99, respectively (Figure 1). Compared to the widely used commercial ELISAs, the arbo-plex MIA agreement was 87% (kappa = 0.64) for DENV against the EUROIMMUN Anti-DENV NS1 type 1–4 IgG ELISA, 94% (kappa = 0.80) for CHIKV against EUROIMMUN Anti-CHKV IgG ELISA and 96% (kappa = 0.94) for RVF against IDVet RVF IgG ELISA (Table 1). Table 1. Comparison of commercial IgG ELISAs with the inhouse arbo-plex Luminex assay Luminex anti-DENV2 NS1 IgG Neg Pos Total Anti-DENV NS1 type 1-4 IgG ELISA (EUROIMMUN) Neg 281 (0.70) 12 (0.03) 293 (0.73) Pos 41 (0.10) 68 (0.17) 109 (0.27) Total 322 (0.80) 80 (0.20) 402 (1.00) Luminex anti-CHK E2 IgG Neg Pos Total Anti-CHKV IgG ELISA (EUROIMMUN) Neg 1295 (0.79) 45 (0.03) 1340 (0.82) Pos 54 (0.03) 244 (0.15) 298 (0.18) Total 1349 (0.82) 289 (0.18) 1638 (1.00) Luminex anti-RVF Gc IgG Neg Pos Total RVF IgG ELISA (IDVet) Neg 410 (0.96) 11 (0.03) 421 (0.99) Pos 5 (0.01) 1 (0.00) 6 (0.01) Total 415 (0.97) 12 (0.03) 427 (1.00) National seroprevalence A total of 11,124 samples collected in 2020 and 2021 from blood donors across the eight regions of Kenya were assayed using the arbo-plex MIA. The Coast region had the greatest number of samples (n=3941, 35.4%) while the North-Eastern region had the least (n=95, 0.9%). The largest age groups in the sample were 15–24 and 25–34 years (n=3972, 35.7%), and males comprised (n=8807, 79.2%) of the sample (Table 2). National crude seroprevalences were 20.9% (95% CI 20.2–21.7) for DENV, 24% (95% CI 23.2–24.8) for CHIKV, and 4.2% (95% CI 3.8–4.5) for RVFV while Bayesian population-weighted and test-adjusted seroprevalences were 7.6% (95% CI, 6.5–9.3) for DENV, 18.4% (95% CI, 15.2–21.8) for CHIKV, and 3.1% (95% CI, 2.2–4.2) for RVFV (Table 2). The seroprevalences for viruses increased with age most for DENV (from approximately 5% to 11%), modestly for CHIKV (16% to 19%) and no increase was evident for RVFV. The geographical distribution for DENV mirrored CHIKV in the Coast with high prevalences 58.2% vs 32.8% and low prevalences centrally 20% in the counties of Western (Kisumu, Siaya, Bungoma, Homabay, Migori, Busia), Central (Nyeri) and North-Eastern (Wajir and Turkana) (Figure 2 and Supplementary Table 2). On the other hand, RVFV seroprevalence was highest at 6% in Nairobi County followed by 5.3% in the North-Eastern region and was low in the Coast at 1.6% (Table 2 and Figure 2). Table 2. Crude, population-weighted (BPW), and population-weighted test performance–adjusted (BPWTA) DENV, CHIKV and RVFV IgG seroprevalence by participant characteristics and regions. DENV CHIKV RVFV All samples (%) Pos Crude% (95 CI) BPW% (95 CI) BPWTA% (95 CI) Pos Crude% (95 CI) BPW% (95 CI) BPWTA% (95 CI) Pos Crude% (95 CI) BPW% (95 CI) BPWTA% (95 CI) Overall 11124 2325 20.9 (20.2–21.7) 8.9 (8.0-10.2) 7.6 (6.5-9.3) 2670 24 (23.2–24.8) 21.5 (19.9-23.3) 18.4 (15.2-21.8) 462 4.2 (3.8–4.5) 4.7 (4.1-5.5) 3.1 (2.2-4.2) Age-group 15–24 3972 (35.7) 573 14.4 (13.4–15.6) 6.5 (5.6-7.6) 5.3 (4.4-6.8) 783 19.7 (18.5–21) 19.5 (17.7-21.5) 16.2 (12.9-19.7) 165 4.2 (3.6–4.8) 4.7 (3.9-5.5) 2.9 (1.8-4.1) 25–34 3973 (35.7) 853 21.5 (20.2–22.8) 8.5 (7.6-9.7) 7.3 (6.3-8.8) 1053 26.5 (25.2–27.9) 23.2 (21.4-25.3) 20.3 (16.8-23.9) 160 4 (3.5–4.7) 4.8 (4.0-5.6) 3.3 (2.2-4.5) 35–44 2191 (19.7) 561 25.6 (23.8–27.5) 10.1 (8.9-11.6) 8.7 (7.2-10.6) 575 26.2 (24.4–28.1) 22.4 (20.4-24.7) 19.4 (15.9-23.2) 101 4.6 (3.8–5.6) 4.9 (4.2-6.0) 3.5 (2.3-5.1) 45–54 794 (7.1) 259 32.6 (29.4–36) 12.8 (10.8-15.3) 11.3 (9.1-14.2) 204 25.7 (22.8–28.8) 21.4 (18.8-24.3) 18.4 (14.5-22.5) 26 3.3 (2.2–4.8) 4.6 (3.4-5.5) 2.9 (1.6-4.3) 55–64 194 (1.7) 79 40.7 (34.1–47.8) 13.3 (9.8-17.7) 11.4 (7.9-16.0) 55 28.4 (22.5–35.1) 21.9 (18.1-26.5) 19.3 (14.3-25.0) 10 5.2 (2.7–9.3) 4.6 (3.6-6.3) 3.1 (1.6-5.4) Sex Female 2317 (20.8) 430 18.6 (17–20.2) 8.3 (7.2-9.7) 7.1 (5.9-8.9) 503 21.7 (20.1–23.4) 20.7 (18.7-23.0) 17.5 (14.0-21.2) 91 3.9 (3.2–4.8) 4.7 (3.9-5.7) 3.2 (2.0-4.6) Male 8807 (79.2) 1895 21.5 (20.7–22.4) 9.5 (8.6-10.9) 8.1 (6.9-9.8) 2167 24.6 (23.7–25.5) 22.3 (20.7-24.0) 19.3 (16.1-22.6) 371 4.2 (3.8–4.7) 4.7 (4.1-5.4) 3.1 (2.2-4.2) Region Rift Valley 1988 (17.9) 57 2.9 (2.2–3.7) 3.2 (2.4-4.0) 1.1 (0.1-2.3) 244 12.3 (10.9–13.8) 12.0 (10.6-13.5) 4.0 (0.6-8.4) 70 3.5 (2.8–4.4) 3.5 (2.7-4.4) 1.2 (0.2-2.7) Eastern 1859 (16.7) 38 2 (1.5–2.8) 2.3 (1.7-3.1) 0.5 (0.0-1.4) 263 14.1 (12.6–15.8) 13.8 (12.2-15.5) 6.2 (2.4-10.7) 80 4.3 (3.5–5.3) 4.3 (3.4-5.2) 1.9 (0.5-3.6) North Eastern 95 (0.9) 8 8.4 (4.1–16.0) 8.4 (4.1-14.9) 7.1 (2.0-14.4) 28 29.5 (21.2–39.3) 26.7 (19.1-35.6) 22.0 (12.1-32.9) 9 9.5 (4.9–17.2) 6.8 (3.6-12.7) 5.3 (0.8-12.1) Coast 3941 (35.4) 2104 53.4 (51.8–54.9) 54.4 (52.5-56.3) 58.2 (54.7-62.8) 1481 37.6 (36.1–39.1) 36.3 (34.6-38.1) 32.8 (29.0-37.0) 157 4 (3.4–4.6) 4.0 (3.3-4.6) 1.6 (0.3-3.1) Nairobi 1089 (9.8) 62 5.7 (4.5–7.2) 6.4 (5.0-8.1) 4.8 (2.9-6.9) 202 18.5 (16.3–21) 19.1 (16.7-21.5) 12.5 (7.9-17.3) 87 8 (6.5–9.8) 7.6 (6.1-9.4) 6.0 (3.9-8.3) Central 938 (8.4) 20 2.1 (1.4–3.3) 2.7 (1.7-4.0) 0.7 (0.0-1.9) 133 14.2 (12.1–16.6) 14.3 (12.1-16.7) 6.4 (1.8-11.5) 28 3 (2.1–4.3) 3.1 (2.1-4.3) 1.0 (0.1-2.5) Nyanza 1088 (9.8) 29 2.7 (1.8–3.8) 3.3 (2.3-4.7) 1.3 (0.1-2.9) 279 25.6 (23.1–28.3) 26.3 (23.6-29.1) 21.7 (17.1-26.5) 29 2.7 (1.8–3.8) 2.8 (2.0-3.8) 1.3 (0.1-2.9) Western 126 (1.1) 7 5.6 (2.5–11.2) 6.3 (2.9-11.8) 4.4 (0.5-10.3) 40 31.7 (24.2–40.3) 30.2 (22.8-38.3) 25.8 (16.1-36.1) 2 1.6 (0.1–6.0) 2.9 (1.1-5.5) 4.4 (0.5-10.3) Discussion This study evaluated the diagnostic performance of an in-house multiplex immunoassay (arbo-plex MIA) for detecting IgG antibodies against DENV, CHIKV and RVFV, and subsequently applied the assay to assess national arboviral seroprevalence using over 11,000 blood donor samples collected across Kenya during 2020 and 2021. The arbo-plex MIA demonstrated strong diagnostic accuracy when benchmarked against FRNT, with AUC values ranging from 0.9148 to 0.9621 across the three viruses. Sensitivity and specificity estimates were similarly robust, particularly for CHIKV and RVFV, supporting the value of multiplex platforms in large-scale epidemiological surveillance. The relatively low specificity of 85.5% of the arbo-plex MIA for DENV may have been caused by the insensitivity of the FRNT 90 assay used as gold standard, as the 90% virus neutralization cut off would mean some true positives were mis-classified as negatives by the FRNT. However, this high cut-off has been preferred in previous literature as a means to improve specificity of the FRNT in the face of cross-reactivity with other flaviviruses [27-29]. Agreement with widely used commercial ELISAs was high (87–96%), highlighting the assay’s reliability and practical relevance for routine sero-epidemiological applications. The DENV crude seroprevalence in 2020/21 was significantly higher (20.9%) than BPWTA seroprevalence (7.6%), reflecting the high number of samples from the Coast region (N=3941, 35.4%) and the high seroprevalence of DENV focused only on the Coast region (58.2%) and not in other regions (<7.1%). The only nationally representative study to date is a 2007 household survey using Kenya AIDS Indicator Survey samples. However, it reported only crude seroprevalence, without population weighting or adjustment for test performance, limiting comparability and leaving potential biases unaddressed. Even so, our findings suggest an approximate 8-percentage-point increase in crude national DENV seroprevalence since 2007 (Supplementary Figure 1). As in the previous survey, the Coast region remained the primary focus of dengue transmission, with more than half of individuals showing evidence of past infection [15]. A closer scrutiny of DENV burden in the Coast region showed Mombasa and Lamu county seroprevalence were the highest 73.5% and 56% respectively, (Supplementary Table 2), consistent with previous outbreaks and vector distribution maps [14]. CHIKV BPWTA seroprevalence was 18.4%, reflecting the widespread CHIKV transmission occurring in most regions of the country. The highest seropositivity in 2020/21 was observed in the Coast region, followed by Western and Nyanza regions, aligning with documented outbreaks in East Africa over the past decade [15, 30, 31]. Compared to 2007 survey, CHIKV crude seroprevalence was similarly elevated nationally at 24%, with marked increases across all regions [15]. Noteworthy is the widespread CHIKV transmission occurring in regions where previously there were no reported CHIKV epidemics, for example in Western Kenya [15]. In contrast, RVFV crude seroprevalence remained largely stable since 2007 (4.2% vs 4.5%), indicating limited incremental transmission at the national level over the past decade. The highest burden continued in the arid and semi-arid regions of North-Eastern and parts of Eastern and Coastal Kenya, consistent with the ecology-driven nature of RVFV outbreaks, which are strongly linked to livestock epizootics and rainfall anomalies [32]. RVF seropositivity patterns across age groups also reflected cumulative exposure. The geographic clustering in historically high-risk pastoralist regions reinforces the importance of targeted surveillance and early warning systems [33, 34]. A limitation of this study is the use of DENV-2 NS1 in detecting circulating IgG antibodies and only DENV-2 virus in the FRNT 90 assay which makes it impossible to determine relative contributions of the other three serotypes, 1, 3 and 4. Although previous reports indicate DENV-2 is the predominant serotype in Kenya, knowledge of the contribution of the other serotypes in dengue burden would be more informative for public health interventions [35, 36]. Another limitation is the use of blood donors as a convenience sample in our study. These were not evenly distributed nationwide; 5 of the 47 counties had no or <20 samples. Furthermore, use of blood donor samples excluded a substantial proportion (43%) of the Kenyan population, children 65 years, where substantial seroprevalence has been reported [19, 30, 37, 38]. However, blood donor samples present a pragmatic, informative and less costly method for national serosurveys for many pathogens including arboviruses [19, 39, 40]. Overall, this study provides updated, nationally representative sero-epidemiological data demonstrating substantial dengue and chikungunya transmission in Kenya and stable but regionally concentrated RVF activity. Our data can inform public health interventions such as targeted vaccine deployment by identifying populations and regions/counties with the greatest susceptibility. By revealing spatial and age-specific immunity patterns, these data may guide prioritization of high-risk groups, optimize timing of vaccination, and inform strategies where prior exposure may influence vaccine performance thereby supporting more efficient, evidence-based allocation of vaccines, maximizing impact in a resource limited setting. The validated arbo-plex multiplex assay offers a reliable and efficient tool for integrated arbovirus surveillance, enabling simultaneous monitoring of multiple pathogens of public health importance. Future studies should incorporate longitudinal sampling and neutralization-based confirmation to refine incidence estimates and expand surveillance into under-represented regions and age groups. Strengthening integrated, multiplex-based surveillance will be critical for timely detection of emerging outbreaks and informing targeted vector control and public health interventions. Methods Study samples and ethical considerations The characteristics of the test sample populations are summarized in Supplementary Table 1. Firstly, a panel of pooled convalescent serum samples (n=10) collected from Kenya, Uganda and Tunisia, as part of a collaborative study meant to assess the suitability for the candidate anti-RVFV WHO International Standard were used for RVFV FRNT 80 analysis and optimization of arbo-plex MIA sensitivity and specificity [41]. Secondly, a cohort of adult samples (n=147) collected in 2018 for the annual cross-sectional surveys for malaria surveillance in Coastal Kenya were used for CHIKV FRNT 50 and RVFV FRNT 80 analysis and optimization of sensitivity and specificity. Two cohorts KIPMAT (n=76), mothers and newborns investigated for risk factors for severe morbidity and mortality in Kilifi and CPGH (n=57), anonymous presumed healthy, adult volunteer blood donors from the Coast General Teaching and Referral Hospital were used for DENV FRNT 90 analysis and optimization of sensitivity and specificity. Samples collected in Kilifi (n=795) and Nairobi (n=843) Health and Demographic Surveillance Systems were used to compare arbo-plex MIA to commercial ELISAs. Finally, countrywide, anonymized, blood donor samples (n=11420) collected by the Kenya National Blood Transfusion Service (KNBTS) were used to estimate seroprevalence of the arboviruses. KNBTS coordinates the collection and screening of blood donations through six regional centers located in Eldoret, Embu, Kisumu, Mombasa, Nairobi, and Nakuru. Each center serves between five and ten of Kenya’s 47 counties. Eligible donors are defined as individuals aged 16–65 years, weighing at least 50 kg, with hemoglobin levels ≥12.5 g/dL, normal blood pressure (systolic 120–129 mmHg and diastolic 80–89 mmHg), a pulse rate of 60–100 beats per minute, and no history of illness within the preceding six months. The blood collection has traditionally relied on voluntary non-remunerated donors recruited through public drives, primarily in high schools, colleges, and universities; however, since September 2019, reduced funding has led to an increased reliance on family replacement donors. All experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research Institute (Protocol SSC 3426). Before the blood draw, donors gave individual consent to use of their samples for research. Ethical approval was obtained for collection, storage and further use for the sample sets used in the validation assays (SERU numbers: 1778, 3149, 3426, 4085). Recombinant antigen production RVFV glycoprotein Gc recombinant protein was expressed in-house with the methods previously described [42]. DENV2 NSP1 (Cat No; DENV2 NS1-500) and CHKV E2 (Cat No; REC31617-500) recombinant antigens were purchased from The Native Antigen Company. Bead conjugation We coupled DENV2 NSP1, CHKV E2 and RVFV Gc antigens onto MagPlex® microspheres regions 75, 48 and 45 respectively. A total of 1mL (1.25 *107 microspheres) of homogeneous bead stock solution for the respective regions were transferred to 1.5mL Protein LoBind tubes (Eppendorf, Cat No; EP0030108116). Using a magnetic separator (Thermofisher, Cat No; 12321D), the beads were washed once with 250µL UltraPure™ DNase/RNase-Free Distilled Water (Invitrogen, Cat No; 10977-015). The beads were resuspended in 100µL bead activation buffer (0.1M sodium phosphate monobasic, pH 6.2) by vortexing followed by a 20s sonication. A total of 50µL of 50mg/mL of freshly prepared Sulfo-NHS (N-hydroxysulfosuccinimide) and 50µL of 50mg/mL EDC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide) were each added to the bead/activation buffer mixture and incubated at RT for 20min with gentle mixing by vortexing at 10min intervals. The beads were subsequently washed with 250µL of coupling buffer (50mM MES, pH 5.0) for a total of two washes, resuspended in 1mL coupling buffer with antigens at final concentration of 190 µg/mL for DENV NS1 and RVF Gc and 128.6 µg/mL for CHIKV E2 mixed respectively and incubated at RT for 2hrs with mixing by rotation. Following the incubation, the beads were washed with 1mL PBS-TBN (1XPBS, 0.05% Tween 20, 1% BSA and 0.1% Sodium Azide) for a total of two washes and finally resuspended in 1mL PBS-TBN for use and storage at 2-8°C. Multiplex immunoassay A bead solution was prepared by mixing 900 beads/antigen/ well in 2.5 mL of PBS-TBN. A final volume of 25µL of the bead mix was dispensed into a 96-well plate (Greiner, Cat No; 655076) followed by 25µL of the controls and samples. The plate was incubated at RT for 1hr on a shaker at 600rpm. Using a handheld magnet, the plate was washed three times with 100µL of PBS-TBN with intermittent shaking at 600rpm for 10s. The plate was dubbed off wash buffer and 50µL of 1:400 R-phycoerythrin conjugated goat anti-human antibody (Jacobson Immunoresearch, Cat No; 109-116-098) in 1XPBS was added and the plate incubated at RT for 30min on a shaker at 600rpm. The plate was washed three times as described above and 100µL of 1X PBS was added and incubated at RT for 5 min on a shaker at 600rpm. The results were acquired on the Luminex 100/200 machine. Focus reduction neutralization test (FRNT) The CHKV, DENV and RVFV FRNT have been described before (Wright, D. et al , 2020, Barsosio, H. C. et a l, 2019) and with some minor modifications, serum samples were diluted in Dulbecco's Modified Eagle Medium containing 10% FCS (D10) to a final dilution of 1:10. The stock virus was diluted to 10 3 FFU in D10 and 100µL of the diluted stock was mixed with 100µL of diluted serum. The mixture was incubated at 37°C for 1hr and plated onto Vero cells at 90% confluence. The mixture was incubated for 2hrs at 37°C with 5% CO 2. The plates were rocked at 15min intervals, and the virus-serum mixture was replaced with 100µL of D10. The plates were then incubated for 24hrs. Following the incubation, the cells were fixed with 4% Paraformaldehyde, washed three times with 200µL of 0.5% Triton X-100 in PBS and blocked with 100µL of casein in PBS. A mouse anti-virus surface antigen primary monoclonal antibody diluted in 0.5% Triton X-100 in PBS was added at 100µL and the plates were incubated for 2hrs at 37°C. The plates were washed as described above and 100µL of secondary antibody, HRP-conjugated anti-mouse antibody was added, and incubated for 1hr at 37°C. The plates were subsequently washed and 100μL of substrate 1X DAB (0.5ml 10X DAB concentrate and 4.5ml Hydrogen) was added to develop foci. A Mabtech IRIS ELISpot reader was used to count the foci. Serum dilution required to reduce virus foci by 90% (DENV FRNT 90 ), 50% (CHIKV FRNT 50 ) and 80% (RVFV FRNT 80 ) compared to virus-only control wells were calculated using the following formula. Commercial ELISA EUROIMMUN Anti-DENV NS1 type 1-4 IgG ELISA (EUROIMMUN Medizinische Labordiagnostika AG, Germany) EUROIMMUN Anti-CHKV IgG ELISA (EUROIMMUN Medizinische Labordiagnostika AG, Germany) and ID Screen ® RVF IgG ELISA (Innovative Diagnostics, France) were performed according to the manufacturer’s instructions. Statical analysis We estimated pathogen-specific seroprevalence using arbo-plex MIA seropositivity cutoffs and a Bayesian multilevel logistic regression analysis that accounted for age-and sex-specific differences in sampling across sites. Assay performance was incorporated by integrating the regression model with a binomial model for the sensitivity and specificity of the arbo-plex MIA, informed by validation data, with uncertainty in test performance propagated to final estimates [19]. To obtain population-representative estimates, modelled age-and sex-specific seroprevalence estimates were post-stratified using population weights reflecting the age and sex distribution of each region or county according to the 2019 Kenya Population and Housing Census data [43]. Bayesian models were implemented in the Stan software [44] and fitted using the rstan package in R v4.5.3 [45]. Posterior inference was based on 10,000 samples across three chains, with convergence assessed using trace plots and the potential scale reduction factor (PSRF) [46]. Declarations Funding declaration This work was supported by the Bill & Melinda Gates Foundation (INV-039626), Wellcome Trust (grant nos. 226141/Z/22/Z and 226130/Z/22/Z). Acknowledgements We thank all the sample donors for their contribution to the research. Author contributions B.K., G.W.M. and J.N. conceptualized and designed the study. B.K., J.G., D.O., D.M. and J.N. conducted the investigation. B.K., S.K.M., B.O., A.M and J.N. performed the formal analysis. J.N., S.U., L.I.O., R.W.S., J.A.G.S., P.B., A.A., G.W.M. and E.W.K. provided resources and secured funding. B.K. wrote the original draft. All authors contributed to manuscript editing and revision. Data availability The datasets and R scripts used for the analysis in this study are available here https://doi.org/10.7910/DVN/3YZ8IE Ethics declarations All experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research Institute (Protocol SSC 3426). Before the blood draw, donors gave individual consent to use of their samples for research. Ethical approval was obtained for collection, storage and further use for the sample sets used in the validation assays (SERU numbers: 1778, 3149 and 4085). Competing interests The authors declare no competing interests. References Kasbergen, L.M.R., et al., The increasing complexity of arbovirus serology: An in-depth systematic review on cross-reactivity. PLOS Neglected Tropical Diseases, 2023. 17 (9): p. e0011651. Endale, A., et al., Magnitude of Antibody Cross-Reactivity in Medically Important Mosquito-Borne Flaviviruses: A Systematic Review. Infection and Drug Resistance, 2021. 14 (null): p. 4291-4299. Mayer, S.V., R.B. Tesh, and N. Vasilakis, The emergence of arthropod-borne viral diseases: A global prospective on dengue, chikungunya and zika fevers. Acta Tropica, 2017. 166 : p. 155-163. Johnson, B., et al., Dengue-2 virus in Kenya. 1982. Konongoi, L., et al., Detection of dengue virus serotypes 1, 2 and 3 in selected regions of Kenya: 2011–2014. Virology journal, 2016. 13 (1): p. 182. Obonyo, M., A. Fidhow, and V. Ofula, Investigation of laboratory confirmed dengue outbreak in north-eastern Kenya, 2011. PloS one, 2018. 13 (6): p. e0198556. Ellis, E.M., et al., A household serosurvey to estimate the magnitude of a dengue outbreak in Mombasa, Kenya, 2013. PLoS neglected tropical diseases, 2015. 9 (4): p. e0003733. Muthanje, E.M., et al., March 2019 dengue fever outbreak at the Kenyan south coast involving dengue virus serotype 3, genotypes III and V. PLOS global public health, 2022. 2 (3): p. e0000122. Sergon, K., et al., Seroprevalence of chikungunya virus (CHIKV) infection on Lamu Island, Kenya, October 2004. The American journal of tropical medicine and hygiene, 2008. 78 (2): p. 333-337. Konongoi, S.L., et al., Human and entomologic investigations of chikungunya outbreak in Mandera, Northeastern Kenya, 2016. PloS one, 2018. 13 (10): p. e0205058. Daubney, R. and J. Hudson, Enzootic Hepatitis or Rift Valley Fever. An Un-described Virus Disease of Sheep, Cattle and Man from East Africa. 1931. Sang, R., et al., Rift Valley fever virus epidemic in Kenya, 2006/2007: the entomologic investigations. The American journal of tropical medicine and hygiene, 2010. 83 (2 Suppl): p. 28. Nguku, P.M., et al., An investigation of a major outbreak of Rift Valley fever in Kenya: 2006–2007. The American journal of tropical medicine and hygiene, 2010. 83 (2 Suppl): p. 05. Muchiri, S.K., et al., Predicting the ecological niches of Aedes aegypti s.l. using maximum entropy in Kenya. Frontiers in Tropical Diseases, 2025. Volume 6 - 2025 . Ochieng, C., et al., Seroprevalence of Infections with Dengue, Rift Valley Fever and Chikungunya Viruses in Kenya, 2007. PLOS ONE, 2015. 10 (7): p. e0132645. Bayrau, B.A., et al., Risk factors associated with dengue and chikungunya seroprevalence and seroconversion among urban populations in western and coastal Kenya. PLoS Negl Trop Dis, 2025. 19 (11): p. e0013740. Khan, A., et al., Spatiotemporal overlapping of dengue, chikungunya, and malaria infections in children in Kenya. BMC Infectious Diseases, 2023. 23 (1): p. 183. Kerkhof, K., et al., Reliable Serological Diagnostic Tests for Arboviruses: Feasible or Utopia? Trends in Microbiology, 2020. 28 (4): p. 276-292. Uyoga, S., et al., Seroprevalence of anti–SARS-CoV-2 IgG antibodies in Kenyan blood donors. Science, 2021. 371 (6524): p. 79-82. Blacksell, S.D., et al., Comparison of Seven Commercial Antigen and Antibody Enzyme-Linked Immunosorbent Assays for Detection of Acute Dengue Infection. Clinical and Vaccine Immunology, 2012. 19 (5): p. 804-810. Cêtre-Sossah, C., et al., Evaluation of a commercial competitive ELISA for the detection of antibodies to Rift Valley fever virus in sera of domestic ruminants in France. Preventive Veterinary Medicine, 2009. 90 (1): p. 146-149. Kikuti, M., et al., Evaluation of two commercially available chikungunya virus IgM enzyme-linked immunoassays (ELISA) in a setting of concomitant transmission of chikungunya, dengue and Zika viruses. International Journal of Infectious Diseases, 2020. 91 : p. 38-43. Felix, A.C., et al., Cross reactivity of commercial anti-dengue immunoassays in patients with acute Zika virus infection. Journal of Medical Virology, 2017. 89 (8): p. 1477-1479. Andrew, A., et al., Diagnostic accuracy of serological tests for the diagnosis of Chikungunya virus infection: A systematic review and meta-analysis. PLOS Neglected Tropical Diseases, 2022. 16 (2): p. e0010152. Lopez, A.L., et al., Determining dengue virus serostatus by indirect IgG ELISA compared with focus reduction neutralisation test in children in Cebu, Philippines: a prospective population-based study. The Lancet Global Health, 2021. 9 (1): p. e44-e51. Jäckel, S., et al., A novel indirect ELISA based on glycoprotein Gn for the detection of IgG antibodies against Rift Valley fever virus in small ruminants. Research in Veterinary Science, 2013. 95 (2): p. 725-730. Karanja, H.K., et al., Temporal trends of dengue seroprevalence among children in coastal Kenya, 1998–2018: a longitudinal cohort study. medRxiv, 2023: p. 2023.08.13.23294039. Wilson, H.L., et al., Neutralization Assay for Zika and Dengue Viruses by Use of Real-Time-PCR-Based Endpoint Assessment. Journal of Clinical Microbiology, 2017. 55 (10): p. 3104-3112. Barsosio, H.C., et al., Congenital microcephaly unrelated to flavivirus exposure in coastal Kenya. Wellcome Open Res, 2019. 4 : p. 179. Tariq, A., et al., Understanding the factors contributing to dengue virus and chikungunya virus seropositivity and seroconversion among children in Kenya. PLOS Neglected Tropical Diseases, 2024. 18 (11): p. e0012616. LaBeaud, A.D., et al., High Rates of O’Nyong Nyong and Chikungunya Virus Transmission in Coastal Kenya. PLOS Neglected Tropical Diseases, 2015. 9 (2): p. e0003436. Munyua, P.M., et al., Predictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya. PLOS ONE, 2016. 11 (1): p. e0144570. Anyangu, A.S., et al., Risk factors for severe Rift Valley fever infection in Kenya, 2007. Am J Trop Med Hyg, 2010. 83 (2 Suppl): p. 14-21. Clark, M.H.A., et al., Systematic literature review of Rift Valley fever virus seroprevalence in livestock, wildlife and humans in Africa from 1968 to 2016. PLOS Neglected Tropical Diseases, 2018. 12 (7): p. e0006627. Nyathi, S., et al., Molecular epidemiology and evolutionary characteristics of dengue virus 2 in East Africa. Nature Communications, 2024. 15 (1): p. 7832. Shah, M.M., et al., High Dengue Burden and Circulation of 4 Virus Serotypes among Children with Undifferentiated Fever, Kenya, 2014-2017. Emerg Infect Dis, 2020. 26 (11): p. 2638-2650. Inziani, M., et al., Seroprevalence of yellow fever, dengue, West Nile and chikungunya viruses in children in Teso South Sub-County, Western Kenya. International Journal of Infectious Diseases, 2020. 91 : p. 104-110. Bayrau, B.A., et al., Risk factors associated with dengue and chikungunya seroprevalence and seroconversion among urban populations in western and coastal Kenya. PLOS Neglected Tropical Diseases, 2025. 19 (11): p. e0013740. Eick, S.M., et al., Seroprevalence of Dengue and Zika Virus in Blood Donations: A Systematic Review. Transfusion Medicine Reviews, 2019. 33 (1): p. 35-42. Aubry, M., et al., Seroprevalence of arboviruses among blood donors in French Polynesia, 2011–2013. International Journal of Infectious Diseases, 2015. 41 : p. 11-12. Emma M. Bentley, V.B., Stephanie Routley, Catherine Cherry, Federica Marchesin, Sarah Kempster, William Elsley, Mark Hassall, Eleanor Atkinson, Peter Rigsby, Mark Page, Wendy Boone, Joseph Sgherza, Vicki Chilton, Julius Lutwama, Francis Mutuku, Jeza Victor, Jael Sagina, Paul Kristiansen, Giada Mattiuzzo, Establishment of the First WHO International Standard for anti-Rift Valley fever virus antibody. WHO, 2023. WHO/BS/2023.2449 . Wright, D., et al., Naturally Acquired Rift Valley Fever Virus Neutralizing Antibodies Predominantly Target the Gn Glycoprotein. iScience, 2020. 23 (11): p. 101669. Kenya National Bureau of Statistics, 2019 Kenya Population and Housing Census. Volume 1. Population by County and sub-County (Government of Kenya, Nairobi). 2019. Team, S.D., RStan: the R interface to Stan. R package version 2.32.7. . 2025. Team, R.C., R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2026. Gelman, A. and J. Hill, Data analysis using regression and multilevel/hierarchical models . 2007: Cambridge university press. Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9294911","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616483638,"identity":"22fa1bb7-bf3d-4b33-9ae4-45069f2850b6","order_by":0,"name":"James Nyagwange","email":"data:image/png;base64,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","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":true,"prefix":"","firstName":"James","middleName":"","lastName":"Nyagwange","suffix":""}],"badges":[],"createdAt":"2026-04-01 17:40:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9294911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9294911/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106066459,"identity":"838fece9-381c-45ef-8106-fecaf47de58a","added_by":"auto","created_at":"2026-04-03 05:29:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eReceiver Operator Characteristic Curves for DENV, CHIKV and RVFV showing areas under the curve of 0.91, 0.96 and 0.96 for DENV, CHIKV and RVFV respectively.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9294911/v1/53e666ddefd51292d58142a9.jpg"},{"id":106066461,"identity":"c6542408-efce-41cb-8735-917917caeabb","added_by":"auto","created_at":"2026-04-03 05:29:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199415,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMap of Kenya showing the national boundary and boundaries of the 47 counties sampled in this study (A). DENV, CHIKV and RVFV seroprevalence estimates by county in 2020/21 (B).\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9294911/v1/75bc26863d0df60580f22289.jpg"},{"id":106414865,"identity":"2030f1a7-e82d-4640-9ae2-fc7ace5285e8","added_by":"auto","created_at":"2026-04-08 10:29:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1410119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9294911/v1/84109594-1f0a-4ece-92b8-bd204b8df8d5.pdf"},{"id":106066460,"identity":"76e1f625-a8b1-4f96-9446-5bfc9ac732c4","added_by":"auto","created_at":"2026-04-03 05:29:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":517272,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9294911/v1/2aafbbd3fcca59f1dcd52f9a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Seroprevalence of Dengue, Chikungunya and Rift Valley fever virus IgG antibodies in Kenyan blood donors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global impact of Aedes borne arboviruses has increased steadily, driven by their rapid geographic expansion, the emergence of new variants, and complications following sequential exposure by viruses such as DENV and Zika causing enhanced disease severity [1, 2]. This spread is fueled by climate change, urbanization, increased connectivity and invasive vector species [1, 3].\u003c/p\u003e\n\u003cp\u003eIn Kenya, the arboviruses responsible for the most significant human infections and public health impact are DENV, CHIKV, and RVFV. DENV was first reported in Malindi in 1982 [4] and has since caused multiple outbreaks, including in Mombasa (2013, 2014, 2017, 2019, 2021), Mandera (2011), Wajir (2017), and Lamu (2021), all in the North East and South East of Kenya [5-8]. CHIKV was first reported in Lamu in 2004, with subsequent outbreaks in Mombasa (2004, 2018, 2022) and Mandera (2016) [9, 10]. RVFV, first identified in 1930 near Lake Naivasha [11], caused major outbreaks in 1997\u0026ndash;1998, 2006\u0026ndash;2007 [12, 13], with a more widespread recent outbreak in 2018 affecting Garissa, Kajiado, Kitui, Marsabit, Tana River, and Wajir, where the human case fatality ratio was approximately 23% [14]. Despite these outbreaks, national estimates of the population burden are lacking; the only nationally representative serosurvey was conducted in 2007 [15], and sero-studies done in 2019 were limited to specific regions of the country [16, 17]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Surveillance of these viruses by clinical, serological or molecular methods is important for planning and implementation of control measures. However, these viruses present with similar clinical symptoms complicating the use of presumed diagnosis in surveillance figures [18]. Molecular methods targeting nucleic acid amplification are useful, but patients are only positive for a short period of time during the viremic phase [1, 18]. Serological methods target persistent antibodies produced following an infection, thus allowing surveillance of the viruses\u0026rsquo; for several months or years after their initial infection [18]. Serological data are useful in informing public health responses, such as targeted vaccine deployment, by identifying spatial, demographic, and temporal heterogeneity in population immunity and susceptibility. Seroprevalence can also be useful in approximating cumulative incidence at the onset of a new epidemic such as COVID-19, and, in the absence of robust clinical surveillance, can be useful in inferring the transmission of the virus in a population [19].\u003c/p\u003e\n\u003cp\u003eSeveral serological methods have been employed in the surveillance of chikungunya (CHIKV), dengue (DENV) and Rift Valley Fever virus (RVFV). Among them are standard enzyme-linked immunosorbent assays (ELISA) used to measure antibodies against target protein antigens [20-22]. Although commercially available, they are expensive and time-consuming. ELISAs allow analysis of only a single antigen at a time and cumulatively require a large sample. Furthermore cross-reactivity among arboviruses compromises diagnosis and interpretation of data acquired using these assays [23]. \u0026nbsp;Neutralization assays such as Foci Reduction Neutralization Test (FRNT) are considered the gold standard in the serology of arboviruses [24-26]. However, neutralization assays are laborious and time consuming and not feasible for large serosurveillance studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we have developed a multiplex bead-based immunoassay (MIA), arbo-plex for simultaneous detection of CHIKV, DENV and RVFV, IgG antibodies based on a Luminex\u0026reg; platform (Luminex Corp.), validated it with a wide range of samples and used FRNT as gold standard to define the sensitivity and specificity of the developed arbo-plex MIA. This has allowed us to take advantage of the arbo-plex to simultaneously assay the three pathogens, thereby saving on cost, time and sample volumes while detecting antibodies specific to the three viruses based on the FRNT gold standard assay. To further validate the in-house arbo-plex MIA, we compared it to widely used commercial ELISAs. We then used nationally representative blood donor samples to estimate seroprevalence of DENV, CHIKV and RVFV IgG antibodies in Kenya.\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssay validation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing FRNT as the gold standard, we selected cut-offs for the arbo-plex MIA where most of the samples were classified correctly. The sensitivity and specificity of the in-house arbo-plex MIA was 91.2% (52 of 57; 95% CI 80.1\u0026ndash;96.4%) and 85.5% (65 of 76; 95% CI 75.4\u0026ndash;91.9%) respectively, for DENV with 87.9% of the samples correctly classified, 94.2% (81 of 86; 95% CI 86.6\u0026ndash;97.6%) and 91.8% (56 of 61; 95% CI 81.4\u0026ndash;96.6%) for CHIKV with 93.2% of the samples correctly classified and 100% (10 of 10; 95% CI 69.1\u0026ndash;100%) and 95.2% (140 of 147; 95% CI 90.2\u0026ndash;97.7%) for RVFV with 95.3% of the samples correctly classified. The Receiver Operating Characteristic curves for DENV, CHIKV and RVFV had AUC of 0.91, 0.96 and 0.99, respectively (Figure 1).\u003c/p\u003e\n\u003cp\u003eCompared to the widely used commercial ELISAs, the arbo-plex MIA agreement was 87% (kappa = 0.64) for DENV against the EUROIMMUN Anti-DENV NS1 type 1\u0026ndash;4 IgG ELISA, 94% (kappa = 0.80) for CHIKV against EUROIMMUN Anti-CHKV IgG ELISA and 96% (kappa = 0.94) for RVF against IDVet RVF IgG ELISA (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Comparison of commercial IgG ELISAs with the inhouse arbo-plex Luminex assay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 375px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminex anti-DENV2 NS1 IgG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-DENV NS1 type 1-4 IgG ELISA (EUROIMMUN)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNeg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e281 (0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e12 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e293 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ePos\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e41 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e68 (0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e109 (0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e322 (0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e80 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e402 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 375px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminex anti-CHK E2 IgG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-CHKV IgG ELISA (EUROIMMUN)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNeg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1295 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e45 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1340 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ePos\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e54 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e244 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e298 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1349 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e289 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1638 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 375px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminex anti-RVF Gc IgG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eNeg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRVF IgG ELISA (IDVet)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eNeg\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e410 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e11 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e421 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003ePos\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e5 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e1 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e6 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003eTotal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e415 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e12 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e427 (1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNational seroprevalence\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 11,124 samples collected in 2020 and 2021 from blood donors across the eight regions of Kenya were assayed using the arbo-plex MIA. The Coast region had the greatest number of samples (n=3941, 35.4%) while the North-Eastern region had the least (n=95, 0.9%). The largest age groups in the sample were 15\u0026ndash;24 and 25\u0026ndash;34 years (n=3972, 35.7%), and males comprised (n=8807, 79.2%) of the sample (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNational crude seroprevalences were 20.9% (95% CI 20.2\u0026ndash;21.7) for DENV, 24% (95% CI 23.2\u0026ndash;24.8) for CHIKV, and 4.2% (95% CI 3.8\u0026ndash;4.5) for RVFV while Bayesian population-weighted and test-adjusted seroprevalences were 7.6% (95% CI, 6.5\u0026ndash;9.3) for DENV, 18.4% (95% CI, 15.2\u0026ndash;21.8) for CHIKV, and 3.1% (95% CI, 2.2\u0026ndash;4.2) for RVFV (Table 2). \u0026nbsp; The seroprevalences for viruses increased with age most for DENV (from approximately 5% to 11%), modestly for CHIKV (16% to 19%) and no increase was evident for RVFV. \u0026nbsp;The geographical distribution for DENV mirrored CHIKV in the Coast with high prevalences 58.2% vs 32.8% and low prevalences centrally \u0026lt;6.5%. However, there was wider geographical distribution for CHIKV with high seroprevalence \u0026gt;20% in the counties of Western (Kisumu, Siaya, Bungoma, Homabay, Migori, Busia), Central (Nyeri) and North-Eastern (Wajir and Turkana) (Figure 2 and Supplementary Table 2). \u0026nbsp;On the other hand, RVFV seroprevalence was highest at 6% in Nairobi County followed by 5.3% in the North-Eastern region and was low in the Coast at 1.6% (Table 2 and Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2. Crude, population-weighted (BPW), and population-weighted test performance\u0026ndash;adjusted (BPWTA) DENV, CHIKV and RVFV IgG seroprevalence by participant characteristics and regions.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDENV\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHIKV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRVFV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eAll samples (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 8px;\"\u003e\n \u003cp\u003eCrude% (95 CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPW% (95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPWTA%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eCrude%\u003c/p\u003e\n \u003cp\u003e(95 CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPW%\u003c/p\u003e\n \u003cp\u003e(95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPWTA%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eCrude%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95 CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPW%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBPWTA%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(95 CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e11124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003cp\u003e(20.2\u0026ndash;21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003cp\u003e(8.0-10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e(6.5-9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(23.2\u0026ndash;24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003cp\u003e(19.9-23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003cp\u003e(15.2-21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003cp\u003e(3.8\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(4.1-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e(2.2-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge-group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e15\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3972 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003cp\u003e(13.4\u0026ndash;15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e(5.6-7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003cp\u003e(4.4-6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003cp\u003e(18.5\u0026ndash;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003cp\u003e(17.7-21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e16.2\u003c/p\u003e\n \u003cp\u003e(12.9-19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003cp\u003e(3.6\u0026ndash;4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(3.9-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e(1.8-4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3973 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003cp\u003e(20.2\u0026ndash;22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003cp\u003e(7.6-9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003cp\u003e(6.3-8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e26.5\u003c/p\u003e\n \u003cp\u003e(25.2\u0026ndash;27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003cp\u003e(21.4-25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003cp\u003e(16.8-23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(3.5\u0026ndash;4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003cp\u003e(4.0-5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003cp\u003e(2.2-4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2191 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003cp\u003e(23.8\u0026ndash;27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e(8.9-11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003cp\u003e(7.2-10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003cp\u003e(24.4\u0026ndash;28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003cp\u003e(20.4-24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e(15.9-23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003cp\u003e(3.8\u0026ndash;5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003cp\u003e(4.2-6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e(2.3-5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e794 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003cp\u003e(29.4\u0026ndash;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003cp\u003e(10.8-15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003cp\u003e(9.1-14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003cp\u003e(22.8\u0026ndash;28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003cp\u003e(18.8-24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003cp\u003e(14.5-22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;3.3\u003c/p\u003e\n \u003cp\u003e(2.2\u0026ndash;4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003cp\u003e(3.4-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e(1.6-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e194 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003cp\u003e(34.1\u0026ndash;47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003cp\u003e(9.8-17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003cp\u003e(7.9-16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003cp\u003e(22.5\u0026ndash;35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003cp\u003e(18.1-26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e(14.3-25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003cp\u003e(2.7\u0026ndash;9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003cp\u003e(3.6-6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e(1.6-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2317 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003cp\u003e(17\u0026ndash;20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003cp\u003e(7.2-9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003cp\u003e(5.9-8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003cp\u003e(20.1\u0026ndash;23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003cp\u003e(18.7-23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003cp\u003e(14.0-21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003cp\u003e(3.2\u0026ndash;4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(3.9-5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003cp\u003e(2.0-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8807 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003cp\u003e(20.7\u0026ndash;22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003cp\u003e(8.6-10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8.1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(6.9-9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003cp\u003e(23.7\u0026ndash;25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003cp\u003e(20.7-24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003cp\u003e(16.1-22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003cp\u003e(3.8\u0026ndash;4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(4.1-5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e(2.2-4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRift Valley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1988 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e(2.2\u0026ndash;3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003cp\u003e(2.4-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003cp\u003e(0.1-2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003cp\u003e(10.9\u0026ndash;13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003cp\u003e(10.6-13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e(0.6-8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e(2.8\u0026ndash;4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003cp\u003e(2.7-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003cp\u003e(0.2-2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eEastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1859 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(1.5\u0026ndash;2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003cp\u003e(1.7-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003cp\u003e(0.0-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003cp\u003e(12.6\u0026ndash;15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003cp\u003e(12.2-15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003cp\u003e(2.4-10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003cp\u003e(3.5\u0026ndash;5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003cp\u003e(3.4-5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003cp\u003e(0.5-3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNorth Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e95 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003cp\u003e(4.1\u0026ndash;16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003cp\u003e(4.1-14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003cp\u003e(2.0-14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003cp\u003e(21.2\u0026ndash;39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e26.7\u003c/p\u003e\n \u003cp\u003e(19.1-35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003cp\u003e(12.1-32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003cp\u003e(4.9\u0026ndash;17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003cp\u003e(3.6-12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003cp\u003e(0.8-12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eCoast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e3941 (35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003cp\u003e(51.8\u0026ndash;54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e54.4\u003c/p\u003e\n \u003cp\u003e(52.5-56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003cp\u003e(54.7-62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003cp\u003e(36.1\u0026ndash;39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003cp\u003e(34.6-38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003cp\u003e(29.0-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(3.4\u0026ndash;4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003cp\u003e(3.3-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e(0.3-3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNairobi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1089 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;5.7\u003c/p\u003e\n \u003cp\u003e(4.5\u0026ndash;7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003cp\u003e(5.0-8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003cp\u003e(2.9-6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003cp\u003e(16.3\u0026ndash;21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e19.1\u003c/p\u003e\n \u003cp\u003e(16.7-21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003cp\u003e(7.9-17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(6.5\u0026ndash;9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e(6.1-9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003cp\u003e(3.9-8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e938 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003cp\u003e(1.4\u0026ndash;3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e(1.7-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003cp\u003e(0.0-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003cp\u003e(12.1\u0026ndash;16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003cp\u003e(12.1-16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003cp\u003e(1.8-11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(2.1\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003cp\u003e(2.1-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003cp\u003e(0.1-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eNyanza\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e1088 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e(1.8\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003cp\u003e(2.3-4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(0.1-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003cp\u003e(23.1\u0026ndash;28.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003cp\u003e(23.6-29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003cp\u003e(17.1-26.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e(1.8\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003cp\u003e(2.0-3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003cp\u003e(0.1-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eWestern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e126 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003cp\u003e(2.5\u0026ndash;11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003cp\u003e(2.9-11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e(0.5-10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003cp\u003e(24.2\u0026ndash;40.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003cp\u003e(22.8-38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e(16.1-36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.6\u003c/p\u003e\n \u003cp\u003e(0.1\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003cp\u003e(1.1-5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003cp\u003e(0.5-10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the diagnostic performance of an in-house multiplex immunoassay (arbo-plex MIA) for detecting IgG antibodies against DENV, CHIKV and RVFV, and subsequently applied the assay to assess national arboviral seroprevalence using over 11,000 blood donor samples collected across Kenya during 2020 and 2021. The arbo-plex MIA demonstrated strong diagnostic accuracy when benchmarked against FRNT, with AUC values ranging from 0.9148 to 0.9621 across the three viruses. Sensitivity and specificity estimates were similarly robust, particularly for CHIKV and RVFV, supporting the value of multiplex platforms in large-scale epidemiological surveillance. The relatively low specificity of 85.5% of the arbo-plex MIA for DENV may have been caused by the insensitivity of the FRNT\u003csub\u003e90\u003c/sub\u003e assay used as gold standard, as the 90% virus neutralization cut off would mean some true positives were mis-classified as negatives by the FRNT. However, this high cut-off has been preferred in previous literature as a means to improve specificity of the FRNT in the face of cross-reactivity with other flaviviruses [27-29]. \u0026nbsp;Agreement with widely used commercial ELISAs was high (87\u0026ndash;96%), highlighting the assay\u0026rsquo;s reliability and practical relevance for routine sero-epidemiological applications.\u003c/p\u003e\n\u003cp\u003eThe DENV crude seroprevalence in 2020/21 was significantly higher (20.9%) than BPWTA seroprevalence (7.6%), reflecting the high number of samples from the Coast region (N=3941, 35.4%) and the high seroprevalence of DENV focused only on the Coast region (58.2%) and not in other regions (\u0026lt;7.1%). The only nationally representative study to date is a 2007 household survey using Kenya AIDS Indicator Survey samples. However, it reported only crude seroprevalence, without population weighting or adjustment for test performance, limiting comparability and leaving potential biases unaddressed. Even so, our findings suggest an approximate 8-percentage-point increase in crude national DENV seroprevalence since 2007 (Supplementary Figure 1). As in the previous survey, the Coast region remained the primary focus of dengue transmission, with more than half of individuals showing evidence of past infection [15]. A closer scrutiny of DENV burden in the Coast region showed Mombasa and Lamu county seroprevalence were the highest 73.5% and 56% respectively, (Supplementary Table 2), consistent with previous outbreaks and vector distribution maps [14].\u003c/p\u003e\n\u003cp\u003eCHIKV BPWTA seroprevalence was 18.4%, reflecting the widespread CHIKV transmission occurring in most regions of the country. The highest seropositivity in 2020/21 was observed in the Coast region, followed by Western and Nyanza regions, aligning with documented outbreaks in East Africa over the past decade [15, 30, 31]. Compared to 2007 survey, CHIKV crude seroprevalence was similarly elevated nationally at 24%, with marked increases across all regions [15]. Noteworthy is the widespread CHIKV transmission occurring in regions where previously there were no reported CHIKV epidemics, for example in Western Kenya [15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, RVFV crude seroprevalence remained largely stable since 2007 (4.2% vs 4.5%), indicating limited incremental transmission at the national level over the past decade. The highest burden continued in the arid and semi-arid regions of North-Eastern and parts of Eastern and Coastal Kenya, consistent with the ecology-driven nature of RVFV outbreaks, which are strongly linked to livestock epizootics and rainfall anomalies [32]. RVF seropositivity patterns across age groups also reflected cumulative exposure. The geographic clustering in historically high-risk pastoralist regions reinforces the importance of targeted surveillance and early warning systems [33, 34].\u003c/p\u003e\n\u003cp\u003eA limitation of this study is the use of DENV-2 NS1 in detecting circulating IgG antibodies and only DENV-2 virus in the FRNT\u003csub\u003e90\u003c/sub\u003e assay which makes it impossible to determine relative contributions of the other three serotypes, 1, 3 and 4. Although previous reports indicate DENV-2 is the predominant serotype in Kenya, knowledge of the contribution of the other serotypes in dengue burden would be more informative for public health interventions [35, 36]. Another limitation is the use of blood donors as a convenience sample in our study. These were not evenly distributed nationwide; 5 of the 47 counties had no or \u0026lt;20 samples. Furthermore, use of blood donor samples excluded a substantial proportion (43%) of the Kenyan population, children \u0026lt;15 years and adults \u0026gt;65 years, where substantial seroprevalence has been reported [19, 30, 37, 38]. However, blood donor samples present a pragmatic, informative and less costly method for national serosurveys for many pathogens including arboviruses [19, 39, 40]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, this study provides updated, nationally representative sero-epidemiological data demonstrating substantial dengue and chikungunya transmission in Kenya and stable but regionally concentrated RVF activity. Our data can inform public health interventions such as targeted vaccine deployment by identifying populations and regions/counties with the greatest susceptibility. By revealing spatial and age-specific immunity patterns, these data may guide prioritization of high-risk groups, optimize timing of vaccination, and inform strategies where prior exposure may influence vaccine performance thereby supporting more efficient, evidence-based allocation of vaccines, maximizing impact in a resource limited setting. The validated arbo-plex multiplex assay offers a reliable and efficient tool for integrated arbovirus surveillance, enabling simultaneous monitoring of multiple pathogens of public health importance. Future studies should incorporate longitudinal sampling and neutralization-based confirmation to refine incidence estimates and expand surveillance into under-represented regions and age groups. Strengthening integrated, multiplex-based surveillance will be critical for timely detection of emerging outbreaks and informing targeted vector control and public health interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy samples and ethical considerations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristics of the test sample populations are summarized in Supplementary Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirstly, a panel of pooled convalescent serum samples (n=10) collected from Kenya, Uganda and Tunisia, as part of a collaborative study meant to assess the suitability for the candidate anti-RVFV WHO International Standard were used for RVFV FRNT\u003csub\u003e80\u003c/sub\u003e analysis and optimization of \u0026nbsp;arbo-plex MIA sensitivity and specificity [41]. Secondly, a cohort of adult samples (n=147) collected in 2018 for the annual cross-sectional surveys for malaria surveillance in Coastal Kenya were used for CHIKV FRNT\u003csub\u003e50\u003c/sub\u003e and RVFV FRNT\u003csub\u003e80\u003c/sub\u003e analysis and optimization of sensitivity and specificity. Two cohorts KIPMAT (n=76), mothers and newborns investigated for risk factors for severe morbidity and mortality in Kilifi and CPGH (n=57), anonymous presumed healthy, adult volunteer blood donors from the Coast General Teaching and Referral Hospital were used for DENV FRNT\u003csub\u003e90\u003c/sub\u003e analysis and optimization of sensitivity and specificity. Samples collected in Kilifi (n=795) and Nairobi (n=843) Health and Demographic Surveillance Systems were used to compare arbo-plex MIA to commercial ELISAs. Finally, countrywide, anonymized, blood donor samples (n=11420) collected by the Kenya National Blood Transfusion Service (KNBTS) were used to estimate seroprevalence of the arboviruses. KNBTS coordinates the collection and screening of blood donations through six regional centers located in Eldoret, Embu, Kisumu, Mombasa, Nairobi, and Nakuru. Each center serves between five and ten of Kenya\u0026rsquo;s 47 counties. Eligible donors are defined as individuals aged 16\u0026ndash;65 years, weighing at least 50 kg, with hemoglobin levels \u0026ge;12.5 g/dL, normal blood pressure (systolic 120\u0026ndash;129 mmHg and diastolic 80\u0026ndash;89 mmHg), a pulse rate of 60\u0026ndash;100 beats per minute, and no history of illness within the preceding six months. The blood collection has traditionally relied on voluntary non-remunerated donors recruited through public drives, primarily in high schools, colleges, and universities; however, since September 2019, reduced funding has led to an increased reliance on family replacement donors.\u003c/p\u003e\n\u003cp\u003eAll experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Scientific and Ethics Review Unit (SERU) of the Kenya \u003ca href=\"https://www.sciencedirect.com/topics/medicine-and-dentistry/medical-research\" title=\"Learn more about Medical Research from ScienceDirect's AI-generated Topic Pages\"\u003eMedical Research\u003c/a\u003e Institute (Protocol SSC 3426). Before the blood draw, donors gave individual consent to use of their samples for research. Ethical approval was obtained for collection, storage and further use for the sample sets used in the validation assays (SERU numbers: 1778, 3149, 3426, 4085).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRecombinant antigen production\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;RVFV glycoprotein Gc recombinant protein was expressed in-house with the methods previously described [42]. DENV2 NSP1 (Cat No; DENV2 NS1-500) and CHKV E2 (Cat No; REC31617-500) recombinant antigens were purchased from The Native Antigen Company. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBead conjugation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe coupled DENV2 NSP1, CHKV E2 and RVFV Gc antigens onto MagPlex\u0026reg; microspheres regions 75, 48 and 45 respectively. A total of 1mL (1.25 *107 microspheres) of homogeneous bead stock solution for the respective regions were transferred to 1.5mL Protein LoBind tubes (Eppendorf, Cat No; EP0030108116). Using a magnetic separator (Thermofisher, Cat No; 12321D), the beads were washed once with 250\u0026micro;L UltraPure\u0026trade; DNase/RNase-Free Distilled Water (Invitrogen, Cat No; 10977-015). The beads were resuspended in 100\u0026micro;L bead activation buffer (0.1M sodium phosphate monobasic, pH 6.2) by vortexing followed by a 20s sonication. A total of 50\u0026micro;L of 50mg/mL of freshly prepared Sulfo-NHS (N-hydroxysulfosuccinimide) and 50\u0026micro;L of 50mg/mL EDC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide) were each added to the bead/activation buffer mixture and incubated at RT for 20min with gentle mixing by vortexing at 10min intervals. The beads were subsequently washed with 250\u0026micro;L of coupling buffer (50mM MES, pH 5.0) for a total of two washes, resuspended in 1mL coupling buffer with antigens at final concentration of 190 \u0026micro;g/mL for DENV NS1 and RVF Gc and 128.6 \u0026micro;g/mL for CHIKV E2 mixed respectively and incubated at RT for 2hrs with mixing by rotation. Following the incubation, the beads were washed with 1mL PBS-TBN (1XPBS, 0.05% Tween 20, 1% BSA and 0.1% Sodium Azide) for a total of two washes and finally resuspended in 1mL PBS-TBN for use and storage at 2-8\u0026deg;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMultiplex immunoassay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA bead solution was prepared by mixing 900 beads/antigen/ well in 2.5 mL of PBS-TBN. A final volume of 25\u0026micro;L of the bead mix was dispensed into a 96-well plate (Greiner, Cat No; 655076) followed by 25\u0026micro;L of the controls and samples. The plate was incubated at RT for 1hr on a shaker at 600rpm. Using a handheld magnet, the plate was washed three times with 100\u0026micro;L of PBS-TBN with intermittent shaking at 600rpm for 10s. \u0026nbsp; The plate was dubbed off wash buffer and 50\u0026micro;L of 1:400 R-phycoerythrin conjugated goat anti-human antibody (Jacobson Immunoresearch, Cat No; 109-116-098) in 1XPBS was added and the plate incubated at RT for 30min on a shaker at 600rpm. The plate was washed three times as described above and 100\u0026micro;L of 1X PBS was added and incubated at RT for 5 min on a shaker at 600rpm. The results were acquired on the Luminex 100/200 machine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFocus reduction neutralization test (FRNT)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHKV, DENV and RVFV FRNT have been described before (Wright, D. \u003cem\u003eet al\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003e2020, Barsosio, H. C. \u003cem\u003eet a\u003c/em\u003e\u003cem\u003el,\u0026nbsp;\u003c/em\u003e2019) \u0026nbsp;and with some minor modifications, serum samples were diluted in Dulbecco\u0026apos;s Modified Eagle Medium containing 10% FCS (D10) to a final dilution of 1:10. The stock virus was diluted to 10\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eFFU in D10 and 100\u0026micro;L of the diluted stock was mixed with 100\u0026micro;L of diluted serum. \u0026nbsp;The mixture was incubated at 37\u0026deg;C for 1hr and plated onto Vero cells at 90% confluence. The mixture was incubated for 2hrs at 37\u0026deg;C with 5% CO\u003csub\u003e2.\u003c/sub\u003e The plates were rocked at 15min intervals, and the virus-serum mixture was replaced with 100\u0026micro;L of D10. The plates were then incubated for 24hrs. Following the incubation, the cells were fixed with 4% Paraformaldehyde, washed three times with 200\u0026micro;L of 0.5% Triton X-100 in PBS and blocked with 100\u0026micro;L of casein in PBS. A mouse anti-virus surface antigen primary monoclonal antibody diluted in 0.5% Triton X-100 in PBS was added at 100\u0026micro;L and the plates were incubated for 2hrs at 37\u0026deg;C. The plates were washed as described above and 100\u0026micro;L of secondary antibody, HRP-conjugated anti-mouse antibody was added, and incubated for 1hr at 37\u0026deg;C. The plates were subsequently washed and 100\u0026mu;L of substrate 1X DAB (0.5ml 10X DAB concentrate and 4.5ml Hydrogen) was added to develop foci. A Mabtech IRIS ELISpot reader was used to count the foci. Serum dilution required to reduce virus foci by 90% (DENV FRNT\u003csub\u003e90\u003c/sub\u003e), 50% (CHIKV FRNT\u003csub\u003e50\u003c/sub\u003e) and 80% (RVFV FRNT\u003csub\u003e80\u003c/sub\u003e) compared to virus-only control wells were calculated using the following formula. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 545px; height: 60.7355px;\" width=\"545\" height=\"60.7355\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCommercial ELISA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEUROIMMUN Anti-DENV NS1 type 1-4 IgG ELISA (EUROIMMUN Medizinische Labordiagnostika AG, Germany) EUROIMMUN Anti-CHKV IgG ELISA (EUROIMMUN Medizinische Labordiagnostika AG, Germany) and ID Screen\u003cstrong\u003e\u0026reg;\u003c/strong\u003e RVF IgG ELISA (Innovative Diagnostics, France) were performed according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated pathogen-specific seroprevalence using arbo-plex MIA seropositivity cutoffs and a Bayesian multilevel logistic regression analysis that accounted for age-and sex-specific differences in sampling across sites. Assay performance was incorporated by integrating the regression model with a binomial model for the sensitivity and specificity of the arbo-plex MIA, informed by validation data, with uncertainty in test performance propagated to final estimates [19]. To obtain population-representative estimates, modelled age-and sex-specific seroprevalence estimates were post-stratified using population weights reflecting the age and sex distribution of each region or county according to the 2019 Kenya Population and Housing Census data [43]. Bayesian models were implemented in the Stan software [44] and fitted using the rstan package in R v4.5.3 [45]. Posterior inference was based on 10,000 samples across three chains, with convergence assessed using trace plots and the potential scale reduction factor (PSRF) [46].\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Bill \u0026amp; Melinda Gates Foundation (INV-039626), Wellcome Trust (grant nos. 226141/Z/22/Z and 226130/Z/22/Z).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the sample donors for their contribution to the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.K., G.W.M. and J.N. conceptualized and designed the study. B.K., J.G., D.O., D.M. and J.N. conducted the investigation. B.K., S.K.M., B.O., A.M and J.N. performed the formal analysis. J.N., S.U., L.I.O., R.W.S., J.A.G.S., P.B., A.A., G.W.M. and E.W.K. provided resources and secured funding. B.K. wrote the original draft. All authors contributed to manuscript editing and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and R scripts used for the analysis in this study are available here https://doi.org/10.7910/DVN/3YZ8IE\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were performed in accordance with relevant guidelines and regulations. This study was approved by the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research Institute (Protocol SSC 3426). Before the blood draw, donors gave individual consent to use of their samples for research. Ethical approval was obtained for collection, storage and further use for the sample sets used in the validation assays (SERU numbers: 1778, 3149 and 4085).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKasbergen, L.M.R., et al., \u003cem\u003eThe increasing complexity of arbovirus serology: An in-depth systematic review on cross-reactivity.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2023. \u003cstrong\u003e17\u003c/strong\u003e(9): p. e0011651.\u003c/li\u003e\n\u003cli\u003eEndale, A., et al., \u003cem\u003eMagnitude of Antibody Cross-Reactivity in Medically Important Mosquito-Borne Flaviviruses: A Systematic Review.\u003c/em\u003e Infection and Drug Resistance, 2021. \u003cstrong\u003e14\u003c/strong\u003e(null): p. 4291-4299.\u003c/li\u003e\n\u003cli\u003eMayer, S.V., R.B. Tesh, and N. Vasilakis, \u003cem\u003eThe emergence of arthropod-borne viral diseases: A global prospective on dengue, chikungunya and zika fevers.\u003c/em\u003e Acta Tropica, 2017. \u003cstrong\u003e166\u003c/strong\u003e: p. 155-163.\u003c/li\u003e\n\u003cli\u003eJohnson, B., et al., \u003cem\u003eDengue-2 virus in Kenya.\u003c/em\u003e 1982.\u003c/li\u003e\n\u003cli\u003eKonongoi, L., et al., \u003cem\u003eDetection of dengue virus serotypes 1, 2 and 3 in selected regions of Kenya: 2011\u0026ndash;2014.\u003c/em\u003e Virology journal, 2016. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 182.\u003c/li\u003e\n\u003cli\u003eObonyo, M., A. Fidhow, and V. Ofula, \u003cem\u003eInvestigation of laboratory confirmed dengue outbreak in north-eastern Kenya, 2011.\u003c/em\u003e PloS one, 2018. \u003cstrong\u003e13\u003c/strong\u003e(6): p. e0198556.\u003c/li\u003e\n\u003cli\u003eEllis, E.M., et al., \u003cem\u003eA household serosurvey to estimate the magnitude of a dengue outbreak in Mombasa, Kenya, 2013.\u003c/em\u003e PLoS neglected tropical diseases, 2015. \u003cstrong\u003e9\u003c/strong\u003e(4): p. e0003733.\u003c/li\u003e\n\u003cli\u003eMuthanje, E.M., et al., \u003cem\u003eMarch 2019 dengue fever outbreak at the Kenyan south coast involving dengue virus serotype 3, genotypes III and V.\u003c/em\u003e PLOS global public health, 2022. \u003cstrong\u003e2\u003c/strong\u003e(3): p. e0000122.\u003c/li\u003e\n\u003cli\u003eSergon, K., et al., \u003cem\u003eSeroprevalence of chikungunya virus (CHIKV) infection on Lamu Island, Kenya, October 2004.\u003c/em\u003e The American journal of tropical medicine and hygiene, 2008. \u003cstrong\u003e78\u003c/strong\u003e(2): p. 333-337.\u003c/li\u003e\n\u003cli\u003eKonongoi, S.L., et al., \u003cem\u003eHuman and entomologic investigations of chikungunya outbreak in Mandera, Northeastern Kenya, 2016.\u003c/em\u003e PloS one, 2018. \u003cstrong\u003e13\u003c/strong\u003e(10): p. e0205058.\u003c/li\u003e\n\u003cli\u003eDaubney, R. and J. Hudson, \u003cem\u003eEnzootic Hepatitis or Rift Valley Fever. An Un-described Virus Disease of Sheep, Cattle and Man from East Africa.\u003c/em\u003e 1931.\u003c/li\u003e\n\u003cli\u003eSang, R., et al., \u003cem\u003eRift Valley fever virus epidemic in Kenya, 2006/2007: the entomologic investigations.\u003c/em\u003e The American journal of tropical medicine and hygiene, 2010. \u003cstrong\u003e83\u003c/strong\u003e(2 Suppl): p. 28.\u003c/li\u003e\n\u003cli\u003eNguku, P.M., et al., \u003cem\u003eAn investigation of a major outbreak of Rift Valley fever in Kenya: 2006\u0026ndash;2007.\u003c/em\u003e The American journal of tropical medicine and hygiene, 2010. \u003cstrong\u003e83\u003c/strong\u003e(2 Suppl): p. 05.\u003c/li\u003e\n\u003cli\u003eMuchiri, S.K., et al., \u003cem\u003ePredicting the ecological niches of Aedes aegypti s.l. using maximum entropy in Kenya.\u003c/em\u003e Frontiers in Tropical Diseases, 2025. \u003cstrong\u003eVolume 6 - 2025\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eOchieng, C., et al., \u003cem\u003eSeroprevalence of Infections with Dengue, Rift Valley Fever and Chikungunya Viruses in Kenya, 2007.\u003c/em\u003e PLOS ONE, 2015. \u003cstrong\u003e10\u003c/strong\u003e(7): p. e0132645.\u003c/li\u003e\n\u003cli\u003eBayrau, B.A., et al., \u003cem\u003eRisk factors associated with dengue and chikungunya seroprevalence and seroconversion among urban populations in western and coastal Kenya.\u003c/em\u003e PLoS Negl Trop Dis, 2025. \u003cstrong\u003e19\u003c/strong\u003e(11): p. e0013740.\u003c/li\u003e\n\u003cli\u003eKhan, A., et al., \u003cem\u003eSpatiotemporal overlapping of dengue, chikungunya, and malaria infections in children in Kenya.\u003c/em\u003e BMC Infectious Diseases, 2023. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 183.\u003c/li\u003e\n\u003cli\u003eKerkhof, K., et al., \u003cem\u003eReliable Serological Diagnostic Tests for Arboviruses: Feasible or Utopia?\u003c/em\u003e Trends in Microbiology, 2020. \u003cstrong\u003e28\u003c/strong\u003e(4): p. 276-292.\u003c/li\u003e\n\u003cli\u003eUyoga, S., et al., \u003cem\u003eSeroprevalence of anti\u0026ndash;SARS-CoV-2 IgG antibodies in Kenyan blood donors.\u003c/em\u003e Science, 2021. \u003cstrong\u003e371\u003c/strong\u003e(6524): p. 79-82.\u003c/li\u003e\n\u003cli\u003eBlacksell, S.D., et al., \u003cem\u003eComparison of Seven Commercial Antigen and Antibody Enzyme-Linked Immunosorbent Assays for Detection of Acute Dengue Infection.\u003c/em\u003e Clinical and Vaccine Immunology, 2012. \u003cstrong\u003e19\u003c/strong\u003e(5): p. 804-810.\u003c/li\u003e\n\u003cli\u003eC\u0026ecirc;tre-Sossah, C., et al., \u003cem\u003eEvaluation of a commercial competitive ELISA for the detection of antibodies to Rift Valley fever virus in sera of domestic ruminants in France.\u003c/em\u003e Preventive Veterinary Medicine, 2009. \u003cstrong\u003e90\u003c/strong\u003e(1): p. 146-149.\u003c/li\u003e\n\u003cli\u003eKikuti, M., et al., \u003cem\u003eEvaluation of two commercially available chikungunya virus IgM enzyme-linked immunoassays (ELISA) in a setting of concomitant transmission of chikungunya, dengue and Zika viruses.\u003c/em\u003e International Journal of Infectious Diseases, 2020. \u003cstrong\u003e91\u003c/strong\u003e: p. 38-43.\u003c/li\u003e\n\u003cli\u003eFelix, A.C., et al., \u003cem\u003eCross reactivity of commercial anti-dengue immunoassays in patients with acute Zika virus infection.\u003c/em\u003e Journal of Medical Virology, 2017. \u003cstrong\u003e89\u003c/strong\u003e(8): p. 1477-1479.\u003c/li\u003e\n\u003cli\u003eAndrew, A., et al., \u003cem\u003eDiagnostic accuracy of serological tests for the diagnosis of Chikungunya virus infection: A systematic review and meta-analysis.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2022. \u003cstrong\u003e16\u003c/strong\u003e(2): p. e0010152.\u003c/li\u003e\n\u003cli\u003eLopez, A.L., et al., \u003cem\u003eDetermining dengue virus serostatus by indirect IgG ELISA compared with focus reduction neutralisation test in children in Cebu, Philippines: a prospective population-based study.\u003c/em\u003e The Lancet Global Health, 2021. \u003cstrong\u003e9\u003c/strong\u003e(1): p. e44-e51.\u003c/li\u003e\n\u003cli\u003eJ\u0026auml;ckel, S., et al., \u003cem\u003eA novel indirect ELISA based on glycoprotein Gn for the detection of IgG antibodies against Rift Valley fever virus in small ruminants.\u003c/em\u003e Research in Veterinary Science, 2013. \u003cstrong\u003e95\u003c/strong\u003e(2): p. 725-730.\u003c/li\u003e\n\u003cli\u003eKaranja, H.K., et al., \u003cem\u003eTemporal trends of dengue seroprevalence among children in coastal Kenya, 1998\u0026ndash;2018: a longitudinal cohort study.\u003c/em\u003e medRxiv, 2023: p. 2023.08.13.23294039.\u003c/li\u003e\n\u003cli\u003eWilson, H.L., et al., \u003cem\u003eNeutralization Assay for Zika and Dengue Viruses by Use of Real-Time-PCR-Based Endpoint Assessment.\u003c/em\u003e Journal of Clinical Microbiology, 2017. \u003cstrong\u003e55\u003c/strong\u003e(10): p. 3104-3112.\u003c/li\u003e\n\u003cli\u003eBarsosio, H.C., et al., \u003cem\u003eCongenital microcephaly unrelated to flavivirus exposure in coastal Kenya.\u003c/em\u003e Wellcome Open Res, 2019. \u003cstrong\u003e4\u003c/strong\u003e: p. 179.\u003c/li\u003e\n\u003cli\u003eTariq, A., et al., \u003cem\u003eUnderstanding the factors contributing to dengue virus and chikungunya virus seropositivity and seroconversion among children in Kenya.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2024. \u003cstrong\u003e18\u003c/strong\u003e(11): p. e0012616.\u003c/li\u003e\n\u003cli\u003eLaBeaud, A.D., et al., \u003cem\u003eHigh Rates of O\u0026rsquo;Nyong Nyong and Chikungunya Virus Transmission in Coastal Kenya.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2015. \u003cstrong\u003e9\u003c/strong\u003e(2): p. e0003436.\u003c/li\u003e\n\u003cli\u003eMunyua, P.M., et al., \u003cem\u003ePredictive Factors and Risk Mapping for Rift Valley Fever Epidemics in Kenya.\u003c/em\u003e PLOS ONE, 2016. \u003cstrong\u003e11\u003c/strong\u003e(1): p. e0144570.\u003c/li\u003e\n\u003cli\u003eAnyangu, A.S., et al., \u003cem\u003eRisk factors for severe Rift Valley fever infection in Kenya, 2007.\u003c/em\u003e Am J Trop Med Hyg, 2010. \u003cstrong\u003e83\u003c/strong\u003e(2 Suppl): p. 14-21.\u003c/li\u003e\n\u003cli\u003eClark, M.H.A., et al., \u003cem\u003eSystematic literature review of Rift Valley fever virus seroprevalence in livestock, wildlife and humans in Africa from 1968 to 2016.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2018. \u003cstrong\u003e12\u003c/strong\u003e(7): p. e0006627.\u003c/li\u003e\n\u003cli\u003eNyathi, S., et al., \u003cem\u003eMolecular epidemiology and evolutionary characteristics of dengue virus 2 in East Africa.\u003c/em\u003e Nature Communications, 2024. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 7832.\u003c/li\u003e\n\u003cli\u003eShah, M.M., et al., \u003cem\u003eHigh Dengue Burden and Circulation of 4 Virus Serotypes among Children with Undifferentiated Fever, Kenya, 2014-2017.\u003c/em\u003e Emerg Infect Dis, 2020. \u003cstrong\u003e26\u003c/strong\u003e(11): p. 2638-2650.\u003c/li\u003e\n\u003cli\u003eInziani, M., et al., \u003cem\u003eSeroprevalence of yellow fever, dengue, West Nile and chikungunya viruses in children in Teso South Sub-County, Western Kenya.\u003c/em\u003e International Journal of Infectious Diseases, 2020. \u003cstrong\u003e91\u003c/strong\u003e: p. 104-110.\u003c/li\u003e\n\u003cli\u003eBayrau, B.A., et al., \u003cem\u003eRisk factors associated with dengue and chikungunya seroprevalence and seroconversion among urban populations in western and coastal Kenya.\u003c/em\u003e PLOS Neglected Tropical Diseases, 2025. \u003cstrong\u003e19\u003c/strong\u003e(11): p. e0013740.\u003c/li\u003e\n\u003cli\u003eEick, S.M., et al., \u003cem\u003eSeroprevalence of Dengue and Zika Virus in Blood Donations: A Systematic Review.\u003c/em\u003e Transfusion Medicine Reviews, 2019. \u003cstrong\u003e33\u003c/strong\u003e(1): p. 35-42.\u003c/li\u003e\n\u003cli\u003eAubry, M., et al., \u003cem\u003eSeroprevalence of arboviruses among blood donors in French Polynesia, 2011\u0026ndash;2013.\u003c/em\u003e International Journal of Infectious Diseases, 2015. \u003cstrong\u003e41\u003c/strong\u003e: p. 11-12.\u003c/li\u003e\n\u003cli\u003eEmma M. Bentley, V.B., Stephanie Routley, Catherine Cherry, Federica Marchesin, Sarah Kempster, William Elsley, Mark Hassall, Eleanor Atkinson, Peter Rigsby, Mark Page, Wendy Boone, Joseph Sgherza, Vicki Chilton, Julius Lutwama, Francis Mutuku, Jeza Victor, Jael Sagina, Paul Kristiansen, Giada Mattiuzzo, \u003cem\u003eEstablishment of the First WHO International Standard for anti-Rift Valley fever virus antibody.\u003c/em\u003e WHO, 2023. \u003cstrong\u003eWHO/BS/2023.2449\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eWright, D., et al., \u003cem\u003eNaturally Acquired Rift Valley Fever Virus Neutralizing Antibodies Predominantly Target the Gn Glycoprotein.\u003c/em\u003e iScience, 2020. \u003cstrong\u003e23\u003c/strong\u003e(11): p. 101669.\u003c/li\u003e\n\u003cli\u003eKenya National Bureau of Statistics, \u003cem\u003e2019 Kenya Population and Housing Census. Volume 1. Population by County and sub-County (Government of Kenya, Nairobi).\u003c/em\u003e 2019.\u003c/li\u003e\n\u003cli\u003eTeam, S.D., \u003cem\u003eRStan: the R interface to Stan. R package version 2.32.7. .\u003c/em\u003e 2025.\u003c/li\u003e\n\u003cli\u003eTeam, R.C., \u003cem\u003eR: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.\u003c/em\u003e 2026.\u003c/li\u003e\n\u003cli\u003eGelman, A. and J. Hill, \u003cem\u003eData analysis using regression and multilevel/hierarchical models\u003c/em\u003e. 2007: Cambridge university press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9294911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9294911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Dengue virus (DENV), chikungunya virus (CHIKV) and Rift Valley fever virus (RVFV) continue to cause recurrent outbreaks in East Africa, yet contemporary national seroprevalence data remain absent. We used a new in-house multiplex immunoassay (arbo-plex MIA) to estimate the national burden of arboviral exposure across Kenya. We validated the arbo-plex MIA against Foci Reduction Neutralization Test (FRNT) and commercial IgG ELISAs. Subsequently, we tested 11,124 blood donor samples, collected nationwide from individuals aged 15-64 years in 2020 /2021, for anti-IgG antibodies to DENV, CHIKV and RVFV. Seroprevalence estimates were stratified by age, sex and region.\r\nIn 2020/21, crude national seroprevalence was 20.9% for DENV, 24% for CHIKV and 4.2% for RVFV while Bayesian population-weighted and test-performance-adjusted national seroprevalence was 7.6% for DENV, 18.4% for CHIKV and 3.1% for RVFV. Seropositivity increased with age for all viruses. The highest seroprevalence for DENV (58.2%) and CHIKV (32.8%) occurred in the Coast region, while RVFV burden was greatest in North-Eastern (5.3%). The arbo-plex MIA proved to be a sensitive, specific and operationally efficient platform for integrated arbovirus surveillance. Overall, the findings suggest that approximately one in five people in Kenya had evidence of prior CHIKV exposure by 2020/21, highlighting widespread transmission and a potentially substantial but under-recognized public health burden that warrants urgent attention. In contrast, DENV and RVFV appear more geographically focal, with persistent but clustered transmission patterns, suggesting that targeted surveillance and region-specific control strategies may be more appropriate for their containment.","manuscriptTitle":"Seroprevalence of Dengue, Chikungunya and Rift Valley fever virus IgG antibodies in Kenyan blood donors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-03 05:29:05","doi":"10.21203/rs.3.rs-9294911/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94a52513-2a3e-45d0-a8c8-573196604d8e","owner":[],"postedDate":"April 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65602815,"name":"Health sciences/Medical research/Epidemiology"},{"id":65602816,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2026-04-07T07:20:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-03 05:29:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9294911","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9294911","identity":"rs-9294911","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}