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To improve the storage and transport of clinical specimens for the diagnosis of Neisseria meningitidis (Nm) infections in resource-limited settings, we have evaluated the performance of dried blood spot (DBS) and dried cerebrospinal fluid spot (DCS) assays. DBS and DCS were prepared on filter paper from liquid specimens previously tested for Nm in the United Kingdom. Nm was detected and genogrouped by real-time PCR performed on crude genomic DNA extracted from the DBS (n = 226) and DCS (n = 226) specimens. Targeted whole-genome sequencing was performed on a subset of specimens, DBS (n = 4) and DCS (n = 6). The overall agreement between the analysis of liquid and dried specimens was (94.2%; 95% CI 90.8–96.7) for blood and (96.4%; 95% CI 93.5–98.0) for cerebrospinal fluid. Relative to liquid specimens as the reference, the DBS and DCS assays had sensitivities of (89.1%; 95% CI 82.7–93.8) and (94.2%; 95% CI 88.9–97.5), respectively, and both assays had specificities above 98%. A genogroup was identified by dried specimen analysis for 81.9% of the confirmed meningococcal infections. Near full-length Nm genome sequences (>86%) were obtained for all ten specimens tested which allowed determination of the sequence type, clonal complex, presence of antimicrobial resistance and other meningococcal genotyping. Dried blood and CSF filter spot assays offer a practical alternative to liquid specimens for the molecular and genomic characterisation of invasive meningococcal diseases in low-resource settings.

As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population—e.g., they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis., The proportion of a population that has previously been infected by a pathogen is typically estimated using antibody thresholds adjusted for sensitivity and specificity. Here, the authors present a model-based alternative to threshold methods which accounts for antibody waning and other sources of spectrum bias.

Streptococcus pneumoniae serotype 1 (ST1) was an important cause of invasive pneumococcaldisease (IPD) globally before the introduction of pneumococcal conjugate vaccines (PCVs) containingST1 antigen. The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE)project gathered ST1 IPD surveillance data from sites globally and aimed to estimatePCV10/13 impact on ST1 IPD incidence. We estimated ST1 IPD incidence rate ratios (IRRs) comparingthe pre‐PCV10/13 period to each post‐PCV10/13 year by site using a Bayesian multi‐level, mixedeffectsPoisson regression and all‐site IRRs using a linear mixed‐effects regression (N = 45 sites).Following PCV10/13 introduction, the incidence rate (IR) of ST1 IPD declined among all ages. Aftersix years of PCV10/13 use, the all‐site IRR was 0.05 (95% credibility interval 0.04–0.06) for all ages,0.05 (0.04–0.05) for &lt, 5 years of age, 0.08 (0.06–0.09) for 5–17 years, 0.06 (0.05–0.08) for 18–49 years,0.06 (0.05–0.07) for 50–64 years, and 0.05 (0.04–0.06) for ≥65 years. PCV10/13 use in infant immunizationprograms was followed by a 95% reduction in ST1 IPD in all ages after approximately 6years. Limited data availability from the highest ST1 disease burden countries using a 3+0 scheduleconstrains generalizability and data from these settings are needed.

Serotype-specific surveillance for invasive pneumococcal disease (IPD) is essential for assessing the impact of 10- and 13-valent pneumococcal conjugate vaccines (PCV10/13). The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) project aimed to evaluate the global evidence to estimate the impact of PCV10/13 by age, product, schedule, and syndrome. Here we systematically characterize and summarize the global landscape of routine serotype-specific IPD surveillance in PCV10/13-using countries and describe the subset that are included in PSERENADE. Of 138 countries using PCV10/13 as of 2018, we identified 109 with IPD surveillance systems, 76 of which met PSERENADE data collection eligibility criteria. PSERENADE received data from most (n = 63, 82.9%), yielding 240,639 post-PCV10/13 introduction IPD cases. Pediatric and adult surveillance was represented from all geographic regions but was limited from lower income and high-burden countries. In PSERENADE, 18 sites evaluated PCV10, 42 PCV13, and 17 both, 17 sites used a 3 + 0 schedule, 38 used 2 + 1, 13 used 3 + 1, and 9 used mixed schedules. With such a sizeable and generally representative dataset, PSERENADE will be able to conduct robust analyses to estimate PCV impact and inform policy at national and global levels regarding adult immunization, schedule, and product choice, including for higher valency PCVs on the horizon.

Streptococcus pneumoniae serotype 1 (ST1) was an important cause of invasive pneumococcal disease (IPD) globally before the introduction of pneumococcal conjugate vaccines (PCVs) containing ST1 antigen. The Pneumococcal Serotype Replacement and Distribution Estimation (PSERENADE) project gathered ST1 IPD surveillance data from sites globally and aimed to estimate PCV10/13 impact on ST1 IPD incidence. We estimated ST1 IPD incidence rate ratios (IRRs) comparing the pre-PCV10/13 period to each post-PCV10/13 year by site using a Bayesian multi-level, mixed-effects Poisson regression and all-site IRRs using a linear mixed-effects regression (N = 45 sites). Following PCV10/13 introduction, the incidence rate (IR) of ST1 IPD declined among all ages. After six years of PCV10/13 use, the all-site IRR was 0.05 (95% credibility interval 0.04–0.06) for all ages, 0.05 (0.04–0.05) for

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality., The reported burden of SARS-CoV-2 has been relatively low in tropical Africa compared to Europe and the Americas, but estimating true infection rates is challenging. Here, the authors screen blood donors in Kenya for SARS-CoV-2 antibodies and describe spatiotemporal seroprevalence dynamics.

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality.

The high proportion of SARS-CoV-2 infections that remain undetected presents a challenge to tracking the progress of the pandemic and implementing control measures in Kenya. We determined the prevalence of IgG to SARS-CoV-2 in residual blood samples from mothers attending antenatal care services at 2 referral hospitals in Kenya. We used a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. In Kenyatta National Hospital, Nairobi, seroprevalence in August 2020 was 49.9% (95% CI 42.7-58.0). In Kilifi County Hospital seroprevalence increased from 1.3% (95% CI 0.04-4.7) in September to 11.0% (95% CI 6.2-16.7) in November 2020. There has been substantial, unobserved transmission of SARS-CoV-2 in parts of Nairobi and Kilifi Counties.

BackgroundThere are no data on SARS-CoV-2 seroprevalence in Africa though the COVID-19 epidemic curve and reported mortality differ from patterns seen elsewhere. We estimated the anti-SARS-CoV-2 antibody prevalence among blood donors in Kenya.MethodsWe measured anti-SARS-CoV-2 spike IgG prevalence by ELISA on residual blood donor samples obtained between April 30 and June 16, 2020. Assay sensitivity and specificity were 83% (95% CI 59-96%) and 99.0% (95% CI 98.1-99.5%), respectively. National seroprevalence was estimated using Bayesian multilevel regression and post-stratification to account for non-random sampling with respect to age, sex and region, adjusted for assay performance.ResultsComplete data were available for 3098 of 3174 donors, aged 15-64 years. By comparison with the Kenyan population, the sample over- represented males (82% versus 49%), adults aged 25-34 years (40% versus 27%) and residents of coastal Counties (49% versus 9%). Crude overall seroprevalence was 5.6% (174/3098). Population-weighted, test- adjusted national seroprevalence was 5.2% (95% CI 3.7– 7.1%). Seroprevalence was highest in the 3 largest urban Counties - Mombasa (9.3% [95% CI 6.4-13.2%)], Nairobi (8.5% [95% CI 4.9-13.5%]) and Kisumu (6.5% [95% CI 3.3-11.2%]).ConclusionsWe estimate that 1 in 20 adults in Kenya had SARS-CoV-2 antibodies during the study period. By the median date of our survey, only 2093 COVID-19 cases and 71 deaths had been reported through the national screening system. This contrasts, by several orders of magnitude, with the numbers of cases and deaths reported in parts of Europe and America when seroprevalence was similar.

Background Estimating vaccination coverage and delays are important because these measures can identify at risk sub-populations who can be targeted with interventions and public health policies. This paper sought to determine estimates and risk factors for children in rural western Kenya who did not receive immunization, received immunization with delay, or were severely underimmunized. Methods Caregivers of children aged 12–23 months old were surveyed for immunization history using written records from the immunization booklet. Risk factors for not receiving immunization, delayed immunization, and severe underimmunization were calculated using log-binomial regression. Children were categorized as delayed if a given immunization was received greater than four weeks from the age-appropriate scheduled date. Severely underimmunized children were those who were fully unvaccinated for more than 90 days and had three or more vaccines delayed or not given. Results Immunization coverage for pentavalent1, pentavalent3, measles, and fully immunized child (FIC; BCG, three doses of polio, three doses of pentavalent, and measles vaccines) were 99%, 94%, 83%, and 80%, respectively. Approximately, 10%, 24%, and 29%, of children were delayed for pentavalent1, pentavalent3, and measles, respectively. Each model produced a unique combination of risk factors with only advanced maternal age as a risk factor common to all models. Children with delayed receipt of pentavalent1 were at risk for not receiving pentavalent3 (RR: 5.20; 95%CI 3.48, 7.77), measles vaccine (RR: 1.48; 95%CI 1.12, 1.95), and not achieving FIC (RR: 1.88; 95%CI 1.51, 2.34) compared with children who received pentavalent1 on time. Conclusions Immunization coverage among 12–23 month old children was high, yet a substantial proportion of children were vaccinated with delay. Although vaccine coverage and timeliness are often conceptualized as separate measures, the finding that delayed pentavalent1 receipt was a strong risk factor for not receiving future immunizations indicates the two measures are intertwined.

Background The identification of determinants of vaccination coverage and delays is important because these predictors identify subsets of the population that can be targeted with public health interventions. This study's aims were to determine vaccination coverage and timeliness estimates and to identify risk factors for not being vaccinated or receiving late vaccinations in rural western Kenya. Methods The study was set in 120 villages within the Kenya Medical Research Institute and Centers for Disease Control and Prevention (KEMRI/CDC) surveillance system in rural western Kenya. In March, 2013, we did a cross-sectional survey of women in the study district who had infants aged 12–23 months. We excluded data from women who did not have their child's immunisation booklet and could provide only a verbal report of their child's immunisation history. We calculated risk factors for delayed and non-receipt of immunisation using binomial regression with log link. Infants were categorised as "delayed" if immunisation was received more than 28 days from schedule. Analyses were restricted to first and third dose of pentavalent vaccine (diphtheria, tetanus, and pertussis [DTP], hepatitis B, and Haemophilus influenzae type b), measles vaccine, and full immunisation (BCG, three doses of polio, three doses of pentavalent, and measles vaccines). Findings We surveyed 2495 women and included data from 1748 women (70%). We excluded data from 747 women (30%) who did not have their child's immunisation booklet card present. Immunisation coverage for pentavalent 1, pentavalent 3, measles, and full immunisation were 99%, 95%, 83%, and 80%, respectively. The proportions of infants with delayed pentavalent 1, pentavalent 3, and measles immunisations were 10%, 24%, and 29%, respectively. Risk factors for not receiving vaccination and delayed vaccination were, overall, similar when estimate trends and confidence intervals were taken in to account. Specifically, infants who received pentavalent 1 late were more likely than children who had the first pentavalent vaccine within schedule to not receive pentavalent 3 (adjusted RR 5·60 [95%CI 3·75–8·37]), measles vaccine (1·47 [1·11–1·94]), and full immunisation (1·87 [1·51–2·32]). Adjusted analyses for predictors of immunisation booklet present at time of survey versus verbal report found that women living closer to the health facility, women with younger infants, and those with male infants were more likely to have an immunisation booklet present at time of survey. Interpretation Immunisation coverage for pentavalent 1, pentavalent 3, and measles vaccines was high, but many children receive their vaccinations more than 28 days after the recommended scheduled date. Since late pentavalent 1 vaccination was associated with pentavalent series drop-out, not being immunised against measles, and not being fully immunised, interventions that target infants with delayed pentavalent 1 vaccination may improve overall immunisation coverage. A potential limitation of this study was that almost a third of mothers were excluded because they could provide only a verbal report of immunisation history instead of their child's immunisation booklet. If there were substantial differences in characteristics between those with an immunisation booklet and those without, and these characteristics were associated with vaccination coverage and delay, the reported estimates may be susceptible to bias. Funding The Bill and Melinda Gates Foundation.