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Background: There is uncertainty about the mortality impact of the COVID-19 pandemic in Africa because of poor ascertainment of cases and limited national civil vital registration. We analysed excess mortality from 1st January 2020-5th May 2022 in a Health and Demographic Surveillance Study in Coastal Kenya where the SARS-CoV-2 seroprevalence reached 75% among adults in March 2022 despite vaccine uptake of only 17%. Methods: We modelled expected mortality in 2020-2022 among a population of 306,000 from baseline surveillance data between 2010-2019. We calculated excess mortality as the ratio of observed/expected deaths in 5 age strata for each month and for each national wave of the pandemic. We estimated cumulative mortality risks as the total number of excess deaths in the pandemic per 100,000 population. We investigated observed deaths using verbal autopsy. Findings: We observed 16,236 deaths among 3,410,800 person years between 1st January 2010 and 5th May 2022. Across 5 waves of COVID-19 cases during 1st April 2020-16th April 2022, population excess mortality was 4.1% (95% PI -0.2%, 7.9%). Mortality was elevated among those aged [≥]65 years at 14.3% (95% PI 7.4%, 21.6%); excess deaths coincided with wave 2 (wild-type), wave 4 (Delta) and wave 5 (Omicron BA1). Among children aged 1-14 years there was negative excess mortality of -20.3% (95% PI -29.8%, -8.1%). Verbal autopsy data showed a transient reduction in deaths from acute respiratory infections in 2020 at all ages. For comparison with other studies, cumulative excess mortality risk for January 2020-December 2021, age-standardized to the Kenyan population, was 47.5/100,000. Interpretation: Net excess mortality during the pandemic was substantially lower in Coastal Kenya than in many high income countries. However, adults, aged [≥]65 years, experienced substantial excess mortality suggesting that targeted COVID-19 vaccination of older persons may limit further COVID-19 deaths by protecting the residual pool of naive individuals.
Subject(s)
COVID-19 , Respiratory Tract Infections , Fractures, Open , DeathABSTRACT
Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the B.1.1.318 and B.1.525 variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Our results show how regional connectivity in downsampled regions like Africa can often influence virus transmissions between neighbouring countries. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission in the region, generating actionable information for public health decision makers in the region.
ABSTRACT
Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) exhibits a high mutations rate and monitoring its spread and evolution is essential for global control of the pandemic. This study determined the SARS-CoV-2 variants circulating in Lagos, from July 2021 to January 2022, when the nation experienced its third and fourth waves.The study utilised archived SARS-CoV-2 positive samples and Midnight whole-genome sequencing workflow from Oxford Nanopore Technologies. Six hundred and sixty-six archived SARS-CoV-2 positive samples, 488 community testing and 178 travellers were analysed. Three hundred forty-one sequences samples were assigned lineages, but 327 sequences with doubly verified collection dates recreated the timeline. 86.5% of the samples came from persons between 16 and 60 years old. Two infections with the Omicron variant (BA.1) among community testers were detected in August 2021 and from seven outbound travellers in September 2021. An inbound traveller also had the Omicron variant (BA.1) in September 2021. Thirteen lineages of the Delta variant and four lineages of the Omicron variant were detected.We show that the Omicron variant was in Nigeria before November 2021 and could have caused the short reprieve between the third and fourth waves. Several lineages detected suggest several introductions, highlighting the need for surveillance.
Subject(s)
COVID-19ABSTRACT
ABSTRACT Background Few studies have assessed the benefits of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. Methods We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (> 18 years) population prioritizing roll-out in over 50-year olds (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at $7 per dose and vaccine delivery costs of $3.90-$6.11 per dose. The cost-effectiveness threshold was USD 919. Findings Slow roll-out at 30% coverage largely targets over 50-year-olds and resulted in 54% fewer deaths (8,132(7,914 to 8,373)) than no vaccination and was cost-saving (ICER=US$-1,343 (-1,345 to - 1,341) per DALY averted). Increasing coverage to 50% and 70%, further reduced deaths by 12% (810 (757 to 872) and 5% (282 (251 to 317) but was not cost-effective, using Kenya’s cost-effectiveness threshold ($ 919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=$-1,607 (-1,609 to -1,604) per DALY averted) compared to slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. Interpretation With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective. KEY QUESTIONS What is already known? The COVID-19 pandemic has led to a substantial number of cases and deaths in low-and middle-income countries. COVID-19 vaccines are considered the main strategy of curtailing the pandemic. However, many African nations are still at the early phase of vaccination. Evidence on the cost-effectiveness of COVID-19 vaccines are useful in estimating value for money and illustrate opportunity costs. However, there is a need to balance these economic outcomes against the potential impact of vaccination. What are the new findings? In Kenya, a targeted vaccination strategy that prioritizes those of an older age and is deployed at a rapid rollout speed achieves greater marginal health impacts and is better value for money. Given the existing high-level population protection to COVID-19 due to prior exposure, vaccination of younger adults is less cost-effective in Kenya. What do the new findings imply? Rapid deployment of vaccines during a pandemic averts more cases, hospitalisations, and deaths and is more cost-effective. Against a context of constrained fiscal space for health, it is likely more prudent for Kenya to target those at severe risk of disease and possibly other vulnerable populations rather than to the whole population.
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COVID-19ABSTRACT
Investment in Africa over the past year with regards to SARS-CoV-2 genotyping has led to a massive increase in the number of sequences, exceeding 100,000 genomes generated to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence within their own borders, coupled with a decrease in sequencing turnaround time. Findings from this genomic surveillance underscores the heterogeneous nature of the pandemic but we observe repeated dissemination of SARS-CoV-2 variants within the continent. Sustained investment for genomic surveillance in Africa is needed as the virus continues to evolve, particularly in the low vaccination landscape. These investments are very crucial for preparedness and response for future pathogen outbreaks.
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Background: The impact of COVID-19 on all-cause mortality in sub-Saharan Africa remains unknown. Methods: We monitored mortality among 306,000 residents of Kilifi Health and Demographic Surveillance System, Kenya, through four COVID-19 waves from April 2020-September 2021. We calculated expected deaths using negative binomial regression fitted to baseline mortality data (2010-2019) and calculated excess mortality as observed-minus-expected deaths. We excluded deaths in infancy because of under-ascertainment of births during lockdown. In February 2021, after two waves of wild-type COVID-19, adult seroprevalence of anti-SARS-CoV-2 was 25.1%. We predicted COVID-19-attributable deaths as the product of age-specific seroprevalence, population size and global infection fatality ratios (IFR). We examined changes in cause of death by Verbal Autopsy (VA). Results: Between April 2020 and February 2021, we observed 1,000 deaths against 1,012 expected deaths (excess mortality -1.2%, 95% PI -6.6%, 5.8%). Based on SARS-CoV-2 seroprevalence, we predicted 306 COVID-19-attributable deaths (a predicted excess mortality of 30.6%) within this period. Monthly mortality analyses showed a significant excess among adults aged [≥]45 years in only two months, July-August 2021, coinciding with the fourth (Delta) wave of COVID-19. By September 2021, overall excess mortality was 3.2% (95% PI -0.6%, 8.1%) and cumulative excess mortality risk was 18.7/100,000. By VA, there was a transient reduction in deaths attributable to acute respiratory infections in 2020. Conclusions: Normal mortality rates during extensive transmission of wild-type SARS-CoV-2 through February 2021 suggests that the IFR for this variant is lower in Kenya than elsewhere. We found excess mortality associated with the Delta variant but the cumulative excess mortality risk remains low in coastal Kenya compared to global estimates.
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COVID-19 , Respiratory Tract Infections , DeathABSTRACT
Importance Most of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. Objective To determine the cumulative incidence of infection with SARS-CoV-2, from a randomly selected sample of individuals normally resident at three Health and Demographic Surveillance Systems (HDSSs) in Kenya. Design This was a cross-sectional population-based serosurvey conducted at Kilifi HDSS, Nairobi Urban HDSS, and Manyatta HDSS in Kenya. We selected age-stratified samples at HDSSs in Kilifi, Kisumu and Nairobi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. Setting Kilifi HDSS comprises a predominantly rural population, Manyatta HDSS comprises a predominantly semi-urban population, while Nairobi Urban HDSS comprises an urban population. The total population under regular surveillance at the three sites is ~470,000. Exposure We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally validated assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. Main Outcome and Measures The primary outcome measure was cumulative incidence of infection with SARS-COV-2 virus as evidenced by seropositivity to SARS-CoV-2 whole spike protein. We adjusted our estimates using classical methods and Bayesian modelling to account for assay performance. We performed multivariable logistic regression to test associations between seropositivity and age category, time period and sex. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27years (10-78) and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kilifi, Kisumu and Nairobi, seroprevalences at the beginning of the study were 14.5 % (9.1-21), 36.0 (28.2-44.4) and 32.4 % (23.1-42.4) respectively; at the end they were 27.6 % (21.4-33.9), 42.0 % (34.7-50.0) and 50.2 % (39.7-61.1), respectively. In multivariable logistic regression models that adjusted for sex and period of sample collections, age category was strongly associated with seroprevalence (p<0.001), with the highest seroprevalences being observed in the 35-44 and [≥]65 year age categories. Conclusion There has been substantial unobserved transmission of SARS-CoV-2 in the general population in Kenya. There is wide variation in cumulative incidence by location and age category.
Subject(s)
COVID-19ABSTRACT
Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of SARS-CoV-2 transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or when infection spreads to susceptible sub-populations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of a new higher-transmissibility variant. Reopening schools led to a minor increase in transmission between the second and third waves. Our predictions of current population exposure in Kenya (∼75% June 1st) have implications for a fourth wave and future control strategies. One Sentence Summary COVID-19 spread in Kenya is explained by mixing heterogeneity and a variant less constrained by high population exposure
Subject(s)
COVID-19 , Encephalitis, ArbovirusABSTRACT
BackgroundFew studies have assessed the seroprevalence of antibodies against SARS-CoV-2 among Health Care Workers (HCWs) in Africa. We report findings from a survey among HCWs in three counties in Kenya. MethodsWe recruited 684 HCWs from Kilifi (rural), Busia (rural) and Nairobi (urban) counties. The serosurvey was conducted between 30th July 2020 and 4th December 2020. We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Assay sensitivity and specificity were 93% (95% CI 88-96%) and 99% (95% CI 98-99.5%), respectively. We adjusted prevalence estimates using Bayesian modeling to account for assay performance. ResultsCrude overall seroprevalence was 19.7% (135/684). After adjustment for assay performance seroprevalence was 20.8% (95% CI 17.5-24.4%). Seroprevalence varied significantly (p<0.001) by site: 43.8% (CI 35.8-52.2%) in Nairobi, 12.6% (CI 8.8-17.1%) in Busia and 11.5% (CI 7.2-17.6%) in Kilifi. In a multivariable model controlling for age, sex and site, professional cadre was not associated with differences in seroprevalence. ConclusionThese initial data demonstrate a high seroprevalence of antibodies to SARS-CoV-2 among HCWs in Kenya. There was significant variation in seroprevalence by region, but not by cadre.
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In October 2020, anti-SARS-CoV-2 IgG seroprevalence among truck drivers and their assistants (TDA) in Kenya was 42.3%, higher than among other key populations. TDA transport essential supplies during the COVID-19 pandemic, placing them at increased risk of being infected and of transmitting SARS-CoV-2 infection over a wide geographical area.
Subject(s)
COVID-19ABSTRACT
Background The global Immunisation Agenda 2030 highlights coverage and equity as a strategic priority goal to reach high equitable immunisation coverage at national levels and in all districts. We estimated inequities in full immunisation coverage associated with socioeconomic, geographic, maternal, child, and place of birth characteristics among children aged 12-23 months in Kenya. Methods We analysed full immunisation coverage (1-dose BCG, 3-dose DTP-HepB-Hib (diphtheria, tetanus, pertussis, hepatitis B and Haemophilus influenzae type B), 3-dose polio, 1-dose measles, and 3-dose pneumococcal vaccines) of 3,943 children aged 12–23 months from the 2014 Kenya Demographic and Health Survey. We disaggregated mean coverage by socioeconomic (household wealth, religion, ethnicity), geographic (place of residence, province), maternal (maternal age at birth, maternal education, maternal marital status, maternal household head status), child (sex of child, birth order), and place of birth characteristics, and estimated inequities in full immunisation coverage using bivariate and multivariate logistic regression. Results Immunisation coverage ranged from 82% [81–84] for the third dose of polio to 97.4% [96.7–98.2] for the first dose of DTP-HepB-Hib, while full immunisation coverage was 68% [66–71] in 2014. After controlling for other background characteristics through multivariate logistic regression, children of mothers with primary school education or higher have at least 54% higher odds of being fully immunised compared to children of mothers with no education. Children born in clinical settings had 41% higher odds of being fully immunised compared to children born in home settings. Children in the Coast, Western, Central, and Eastern regions had at least 74% higher odds of being fully immunised compared to children in the North Eastern region, while children in urban areas had 26% lower odds of full immunisation compared to children in rural areas. Children in the middle and richer wealth quintile households were 43–57% more likely to have full immunisation coverage compared to children in the poorest wealth quintile households. Children who were sixth born or higher had 37% lower odds of full immunisation compared to first-born children. Conclusions Children of mothers with no education, born in home settings, in regions with limited health infrastructure, living in poorer households, and of higher birth order are associated with lower rates of full immunisation. Targeted programmes to reach under-immunised children in these subpopulations will lower the inequities in childhood immunisation coverage in Kenya.