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1.
Tegally, Houriiyah, San, James, Cotten, Matthew, Tegomoh, Bryan, Mboowa, Gerald, Martin, Darren, Baxter, Cheryl, Moir, Monika, Lambisia, Arnold, Diallo, Amadou, Amoako, Daniel, Diagne, Moussa, Sisay, Abay, Zekri, Abdel-Rahman, Barakat, Abdelhamid, Gueye, Abdou Salam, Sangare, Abdoul, Ouedraogo, Abdoul-Salam, Sow, Abdourahmane, Musa, Abdualmoniem, Sesay, Abdul, Lagare, Adamou, Kemi, Adedotun-Sulaiman, Abar, Aden Elmi, Johnson, Adeniji, Fowotade, Adeola, Olubusuyi, Adewumi, Oluwapelumi, Adeyemi, Amuri, Adrienne, Juru, Agnes, Ramadan, Ahmad Mabrouk, Kandeil, Ahmed, Mostafa, Ahmed, Rebai, Ahmed, Sayed, Ahmed, Kazeem, Akano, Balde, Aladje, Christoffels, Alan, Trotter, Alexander, Campbell, Allan, Keita, Alpha Kabinet, Kone, Amadou, Bouzid, Amal, Souissi, Amal, Agweyu, Ambrose, Gutierrez, Ana, Page, Andrew, Yadouleton, Anges, Vinze, Anika, Happi, Anise, Chouikha, Anissa, Iranzadeh, Arash, Maharaj, Arisha, Batchi-Bouyou, Armel Landry, Ismail, Arshad, Sylverken, Augustina, Goba, Augustine, Femi, Ayoade, Sijuwola, Ayotunde Elijah, Ibrahimi, Azeddine, Marycelin, Baba, Salako, Babatunde Lawal, Oderinde, Bamidele, Bolajoko, Bankole, Dhaala, Beatrice, Herring, Belinda, Tsofa, Benjamin, Mvula, Bernard, Njanpop-Lafourcade, Berthe-Marie, Marondera, Blessing, Khaireh, Bouh Abdi, Kouriba, Bourema, Adu, Bright, Pool, Brigitte, McInnis, Bronwyn, Brook, Cara, Williamson, Carolyn, Anscombe, Catherine, Pratt, Catherine, Scheepers, Cathrine, Akoua-Koffi, Chantal, Agoti, Charles, Loucoubar, Cheikh, Onwuamah, Chika Kingsley, Ihekweazu, Chikwe, Malaka, Christian Noël, Peyrefitte, Christophe, Omoruyi, Chukwuma Ewean, Rafaï, Clotaire Donatien, Morang’a, Collins, Nokes, James, Lule, Daniel Bugembe, Bridges, Daniel, Mukadi-Bamuleka, Daniel, Park, Danny, Baker, David, Doolabh, Deelan, Ssemwanga, Deogratius, Tshiabuila, Derek, Bassirou, Diarra, Amuzu, Dominic S. Y.; Goedhals, Dominique, Grant, Donald, Omuoyo, Donwilliams, Maruapula, Dorcas, Wanjohi, Dorcas Waruguru, Foster-Nyarko, Ebenezer, Lusamaki, Eddy, Simulundu, Edgar, Ong’era, Edidah, Ngabana, Edith, Abworo, Edward, Otieno, Edward, Shumba, Edwin, Barasa, Edwine, Ahmed, El Bara, Kampira, Elizabeth, Fahime, Elmostafa El, Lokilo, Emmanuel, Mukantwari, Enatha, Cyril, Erameh, Philomena, Eromon, Belarbi, Essia, Simon-Loriere, Etienne, Anoh, Etilé, Leendertz, Fabian, Taweh, Fahn, Wasfi, Fares, Abdelmoula, Fatma, Takawira, Faustinos, Derrar, Fawzi, Ajogbasile, Fehintola, Treurnicht, Florette, Onikepe, Folarin, Ntoumi, Francine, Muyembe, Francisca, Ngiambudulu, Francisco, Zongo Ragomzingba, Frank Edgard, Dratibi, Fred Athanasius, Iyanu, Fred-Akintunwa, Mbunsu, Gabriel, Thilliez, Gaetan, Kay, Gemma, Akpede, George, George, Uwem, van Zyl, Gert, Awandare, Gordon, Schubert, Grit, Maphalala, Gugu, Ranaivoson, Hafaliana, Lemriss, Hajar, Omunakwe, Hannah, Onywera, Harris, Abe, Haruka, Karray, Hela, Nansumba, Hellen, Triki, Henda, Adje Kadjo, Herve Albéric, Elgahzaly, Hesham, Gumbo, Hlanai, mathieu, Hota, Kavunga-Membo, Hugo, Smeti, Ibtihel, Olawoye, Idowu, Adetifa, Ifedayo, Odia, Ikponmwosa, Boubaker, Ilhem Boutiba-Ben, Ssewanyana, Isaac, Wurie, Isatta, Konstantinus, Iyaloo, Afiwa Halatoko, Jacqueline Wemboo, Ayei, James, Sonoo, Janaki, Lekana-Douki, Jean Bernard, Makangara, Jean-Claude, Tamfum, Jean-Jacques, Heraud, Jean-Michel, Shaffer, Jeffrey, Giandhari, Jennifer, Musyoki, Jennifer, Uwanibe, Jessica, Bhiman, Jinal, Yasuda, Jiro, Morais, Joana, Mends, Joana, Kiconco, Jocelyn, Sandi, John Demby, Huddleston, John, Odoom, John Kofi, Morobe, John, Gyapong, John, Kayiwa, John, Okolie, Johnson, Xavier, Joicymara Santos, Gyamfi, Jones, Kofi Bonney, Joseph Humphrey, Nyandwi, Joseph, Everatt, Josie, Farah, Jouali, Nakaseegu, Joweria, Ngoi, Joyce, Namulondo, Joyce, Oguzie, Judith, Andeko, Julia, Lutwama, Julius, O’Grady, Justin, Siddle, Katherine, Victoir, Kathleen, Adeyemi, Kayode, Tumedi, Kefentse, Carvalho, Kevin Sanders, Mohammed, Khadija Said, Musonda, Kunda, Duedu, Kwabena, Belyamani, Lahcen, Fki-Berrajah, Lamia, Singh, Lavanya, Biscornet, Leon, Le.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-334191

ABSTRACT

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. One-Sentence Summary Expanding Africa SARS-CoV-2 sequencing capacity in a fast evolving pandemic.

2.
Lancet Child Adolesc Health ; 6(5): 345-352, 2022 May.
Article in English | MEDLINE | ID: covidwho-1795976

ABSTRACT

Although great improvements in child survival were achieved in the past two decades, progress has been uneven within and across countries, and the COVID-19 pandemic threatens to reverse previous advances. Demographic and epidemiological transitions around the world have resulted in shifts in the causes and distribution of child death and diseases, and many children are living with short-term and long-term chronic illnesses and disabilities. These changes, plus global threats such as pandemics, transnational and national security issues, and climate change, mean that regular monitoring of child health and wellbeing is essential if we are to achieve the Sustainable Development Goals. This Health Policy describes the three-phased process undertaken by the Child Health Accountability Tracking technical advisory group (CHAT) to develop a core set of indicators on child health and wellbeing for global monitoring purposes, and presents CHAT's research recommendations to address data gaps. CHAT reached consensus on 20 core indicators specific to the health sector, which include 11 impact-level indicators and nine outcome-level indicators that cover the topics of: acute conditions and prevention; health promotion and child development; and chronic conditions, disabilities, injuries, and violence against children. An additional six indicators (three impact and three outcome) that capture information on child health issues such as malaria and HIV are recommended; however, these indicators are only relevant to high-burden regions. CHAT's four research priorities will require investments in health information systems and measurement activities. These investments will help to increase data on children aged 5-9 years; develop standard metadata and data collection processes to enable cross-country comparisons and progress assessments over time; reach a global consensus on essential interventions and associated indicators for monitoring emerging priority areas such as child development, chronic conditions, disabilities, and injuries; and implement strategies to increase the uptake of data on child health to improve evidence-based planning, programming, and advocacy efforts.

3.
Vaccine ; 40(13): 2011-2019, 2022 03 18.
Article in English | MEDLINE | ID: covidwho-1740254

ABSTRACT

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.


Subject(s)
COVID-19 Vaccines , COVID-19 , Adult , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Kenya/epidemiology , Vaccination , Vaccination Coverage
4.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329509

ABSTRACT

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

5.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-327501

ABSTRACT

ABSTRACT Background 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. We conducted population-based serosurveys at three Health and Demographic Surveillance Systems (HDSSs) to determine the cumulative incidence of infection with SARS-CoV-2. Methods We selected random age-stratified population-based samples at HDSSs in Kisumu, Nairobi and Kilifi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. No participant had received a COVID-19 vaccine. 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. We adjusted prevalence estimates using classical methods and Bayesian modelling to account for the sampling scheme and assay performance. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27 (10-78) years and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kisumu, Nairobi and Kilifi, seroprevalences (95% CI) at the beginning of the study were 36.0% (28.2-44.4%), 32.4% (23.1-42.4%), and 14.5% (9.1-21%), and respectively;at the end they were 42.0% (34.7-50.0%), 50.2% (39.7-61.1%), and 24.7% (17.5-32.6%), respectively. Seroprevalence was substantially lower among children (<16 years) than among adults at all three sites (p≤0.001). Conclusion By May 2021 in three broadly representative populations of unvaccinated individuals in Kenya, seroprevalence of anti-SARS-CoV-2 IgG was 25-50%. There was wide variation in cumulative incidence by location and age.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-318265

ABSTRACT

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

7.
Clin Infect Dis ; 74(2): 288-293, 2022 01 29.
Article in English | MEDLINE | ID: covidwho-1662110

ABSTRACT

BACKGROUND: Few studies have assessed the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) in Africa. We report findings from a survey among HCWs in 3 counties in Kenya. METHODS: We recruited 684 HCWs from Kilifi (rural), Busia (rural), and Nairobi (urban) counties. The serosurvey was conducted between 30 July and 4 December 2020. We tested for immunoglobulin G antibodies to SARS-CoV-2 spike protein, using enzyme-linked immunosorbent assay. Assay sensitivity and specificity were 92.7 (95% CI, 87.9-96.1) and 99.0% (95% CI, 98.1-99.5), respectively. We adjusted prevalence estimates, using bayesian modeling to account for assay performance. RESULTS: The crude overall seroprevalence was 19.7% (135 of 684). After adjustment for assay performance, seroprevalence was 20.8% (95% credible interval, 17.5%-24.4%). Seroprevalence varied significantly (P < .001) by site: 43.8% (95% credible interval, 35.8%-52.2%) in Nairobi, 12.6% (8.8%-17.1%) in Busia and 11.5% (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. CONCLUSION: These 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.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibodies, Viral , Bayes Theorem , Health Personnel , Humans , Kenya/epidemiology , Seroepidemiologic Studies , Spike Glycoprotein, Coronavirus
8.
J Clin Virol ; 146: 105061, 2022 01.
Article in English | MEDLINE | ID: covidwho-1636045

ABSTRACT

Many SARS-CoV-2 antibody detection assays have been developed but their differential performance is not well described. In this study we compared an in-house (KWTRP) ELISA which has been used extensively to estimate seroprevalence in the Kenyan population with WANTAI, an ELISA which has been approved for widespread use by the WHO. Using a wide variety of sample sets including pre-pandemic samples (negative gold standard), SARS-CoV-2 PCR positive samples (positive gold standard) and COVID-19 test samples from different periods (unknowns), we compared performance characteristics of the two assays. The overall concordance between WANTAI and KWTRP was 0.97 (95% CI, 0.95-0.98). For WANTAI and KWTRP, sensitivity was 0.95 (95% CI 0.90-0.98) and 0.93 (95% CI 0.87-0.96), respectively. Specificity for WANTAI was 0.98 (95% CI, 0.96-0.99) and 0.99 (95% CI 0.96-1.00) while KWTRP specificity was 0.99 (95% CI, 0.98-1.00) and 1.00 using pre-pandemic blood donors and pre-pandemic malaria cross-sectional survey samples respectively. Both assays show excellent characteristics to detect SARS-CoV-2 antibodies.


Subject(s)
COVID-19 , Spike Glycoprotein, Coronavirus , Antibodies, Viral , Cross-Sectional Studies , Enzyme-Linked Immunosorbent Assay , Humans , Immunoglobulin G , Kenya/epidemiology , SARS-CoV-2 , Sensitivity and Specificity , Seroepidemiologic Studies
9.
BMJ Glob Health ; 6(12)2021 12.
Article in English | MEDLINE | ID: covidwho-1561087

ABSTRACT

BACKGROUND: Case management of symptomatic COVID-19 patients is a key health system intervention. The Kenyan government embarked to fill capacity gaps in essential and advanced critical care (ACC) needed for the management of severe and critical COVID-19. However, given scarce resources, gaps in both essential and ACC persist. This study assessed the cost-effectiveness of investments in essential and ACC to inform the prioritisation of investment decisions. METHODS: We employed a decision tree model to assess the incremental cost-effectiveness of investment in essential care (EC) and investment in both essential and ACC (EC +ACC) compared with current healthcare provision capacity (status quo) for COVID-19 patients in Kenya. We used a health system perspective, and an inpatient care episode time horizon. Cost data were obtained from primary empirical analysis while outcomes data were obtained from epidemiological model estimates. We used univariate and probabilistic sensitivity analysis to assess the robustness of the results. RESULTS: The status quo option is more costly and less effective compared with investment in EC and is thus dominated by the later. The incremental cost-effectiveness ratio of investment in essential and ACC (EC+ACC) was US$1378.21 per disability-adjusted life-year averted and hence not a cost-effective strategy when compared with Kenya's cost-effectiveness threshold (US$908). CONCLUSION: When the criterion of cost-effectiveness is considered, and within the context of resource scarcity, Kenya will achieve better value for money if it prioritises investments in EC before investments in ACC. This information on cost-effectiveness will however need to be considered as part of a multicriteria decision-making framework that uses a range of criteria that reflect societal values of the Kenyan society.


Subject(s)
COVID-19 , Cost-Benefit Analysis , Critical Care , Humans , Kenya , SARS-CoV-2
10.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-294038

ABSTRACT

ABSTRACT Background The transmission networks of SARS-CoV-2 in sub-Saharan Africa remain poorly understood. Methods We undertook phylogenetic analysis of 747 SARS-CoV-2 positive samples collected across six counties in coastal Kenya during the first two waves (March 2020 - February 2021). Viral imports and exports from the region were inferred using ancestral state reconstruction (ASR) approach. Results The genomes were classified into 35 Pango lineages, six of which accounted for 79% of the sequenced infections: B.1 (49%), B.1.535 (11%), B.1.530 (6%), B.1.549 (4%), B.1.333 (4%) and B.1.1 (4%). Four identified lineages were Kenya specific. In a contemporaneous global subsample, 990 lineages were documented, 261 for Africa and 97 for Eastern Africa. ASR analysis identified >300 virus location transition events during the period, these comprising: 69 viral imports into Coastal Kenya;93 viral exports from coastal Kenya;and 191 inter-county import/export events. Most international viral imports (58%) and exports (92%) occurred through Mombasa City, a key touristic and commercial Coastal Kenya center;and many occurred prior to June 2020, when stringent local COVID-19 restriction measures were enforced. After this period, local virus transmission dominated, and distinct local phylogenies were seen. Conclusions Our analysis supports moving control strategies from a focus on international travel to local transmission. Funding This work was funded by Wellcome (grant#: 220985) and the National Institute for Health Research (NIHR), project references: 17/63/and 16/136/33 using UK aid from the UK Government to support global health research, The UK Foreign, Commonwealth and Development Office.

11.
Science ; 374(6570): 989-994, 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1526450

ABSTRACT

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. 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 higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban­rural population structure are critical determinants of viral transmission in Kenya.


Subject(s)
COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , COVID-19 Nucleic Acid Testing , Communicable Disease Control , Epidemics , Humans , Incidence , Kenya/epidemiology , Models, Biological , Seroepidemiologic Studies , Social Class , Socioeconomic Factors
12.
BMJ Glob Health ; 6(5)2021 05.
Article in English | MEDLINE | ID: covidwho-1504118

ABSTRACT

BACKGROUND: Most of the deaths among neonates in low-income and middle-income countries (LMICs) can be prevented through universal access to basic high-quality health services including essential facility-based inpatient care. However, poor routine data undermines data-informed efforts to monitor and promote improvements in the quality of newborn care across hospitals. METHODS: Continuously collected routine patients' data from structured paper record forms for all admissions to newborn units (NBUs) from 16 purposively selected Kenyan public hospitals that are part of a clinical information network were analysed together with data from all paediatric admissions ages 0-13 years from 14 of these hospitals. Data are used to show the proportion of all admissions and deaths in the neonatal age group and examine morbidity and mortality patterns, stratified by birth weight, and their variation across hospitals. FINDINGS: During the 354 hospital months study period, 90 222 patients were admitted to the 14 hospitals contributing NBU and general paediatric ward data. 46% of all the admissions were neonates (aged 0-28 days), but they accounted for 66% of the deaths in the age group 0-13 years. 41 657 inborn neonates were admitted in the NBUs across the 16 hospitals during the study period. 4266/41 657 died giving a crude mortality rate of 10.2% (95% CI 9.97% to 10.55%), with 60% of these deaths occurring on the first-day of admission. Intrapartum-related complications was the single most common diagnosis among the neonates with birth weight of 2000 g or more who died. A threefold variation in mortality across hospitals was observed for birth weight categories 1000-1499 g and 1500-1999 g. INTERPRETATION: The high proportion of neonatal deaths in hospitals may reflect changing patterns of childhood mortality. Majority of newborns died of preventable causes (>95%). Despite availability of high-impact low-cost interventions, hospitals have high and very variable mortality proportions after stratification by birth weight.


Subject(s)
Hospitals , Infant Mortality , Adolescent , Child , Child, Preschool , Cohort Studies , Humans , Infant , Infant, Newborn , Kenya/epidemiology , Retrospective Studies
14.
BMJ Open ; 11(9): e050995, 2021 09 07.
Article in English | MEDLINE | ID: covidwho-1398696

ABSTRACT

OBJECTIVES: To characterise adoption and explore specific clinical and patient factors that might influence pulse oximetry and oxygen use in low-income and middle-income countries (LMICs) over time; to highlight useful considerations for entities working on programmes to improve access to pulse oximetry and oxygen. DESIGN: A multihospital retrospective cohort study. SETTINGS: All admissions (n=132 737) to paediatric wards of 18 purposely selected public hospitals in Kenya that joined a Clinical Information Network (CIN) between March 2014 and December 2020. OUTCOMES: Pulse oximetry use and oxygen prescription on admission; we performed growth-curve modelling to investigate the association of patient factors with study outcomes over time while adjusting for hospital factors. RESULTS: Overall, pulse oximetry was used in 48.8% (64 722/132 737) of all admission cases. Use rose on average with each month of participation in the CIN (OR: 1.11, 95% CI 1.05 to 1.18) but patterns of adoption were highly variable across hospitals suggesting important factors at hospital level influence use of pulse oximetry. Of those with pulse oximetry measurement, 7% (4510/64 722) had hypoxaemia (SpO2 <90%). Across the same period, 8.6% (11 428/132 737) had oxygen prescribed but in 87%, pulse oximetry was either not done or the hypoxaemia threshold (SpO2 <90%) was not met. Lower chest-wall indrawing and other respiratory symptoms were associated with pulse oximetry use at admission and were also associated with oxygen prescription in the absence of pulse oximetry or hypoxaemia. CONCLUSION: The adoption of pulse oximetry recommended in international guidelines for assessing children with severe illness has been slow and erratic, reflecting system and organisational weaknesses. Most oxygen orders at admission seem driven by clinical and situational factors other than the presence of hypoxaemia. Programmes aiming to implement pulse oximetry and oxygen systems will likely need a long-term vision to promote adoption, guideline development and adherence and continuously examine impact.


Subject(s)
Oximetry , Oxygen , Child , Humans , Hypoxia/diagnosis , Kenya , Prospective Studies , Retrospective Studies
16.
Open Forum Infect Dis ; 8(7): ofab314, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1361796

ABSTRACT

In October 2020, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G seroprevalence among truck drivers and their assistants (TDA) in Kenya was 42.3%, higher than among healthcare workers and blood donors. Truck drivers and their assistants transport essential supplies during the coronavirus disease 2019 pandemic, placing them at increased risk of being infected and of transmitting SARS-CoV-2 over a wide geographical area.

17.
Nat Commun ; 12(1): 4809, 2021 08 10.
Article in English | MEDLINE | ID: covidwho-1351953

ABSTRACT

Genomic surveillance of SARS-CoV-2 is important for understanding both the evolution and the patterns of local and global transmission. Here, we generated 311 SARS-CoV-2 genomes from samples collected in coastal Kenya between 17th March and 31st July 2020. We estimated multiple independent SARS-CoV-2 introductions into the region were primarily of European origin, although introductions could have come through neighbouring countries. Lineage B.1 accounted for 74% of sequenced cases. Lineages A, B and B.4 were detected in screened individuals at the Kenya-Tanzania border or returning travellers. Though multiple lineages were introduced into coastal Kenya following the initial confirmed case, none showed extensive local expansion other than lineage B.1. International points of entry were important conduits of SARS-CoV-2 importations into coastal Kenya and early public health responses prevented established transmission of some lineages. Undetected introductions through points of entry including imports from elsewhere in the country gave rise to the local epidemic at the Kenyan coast.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Genome, Viral , SARS-CoV-2/genetics , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/transmission , Child , Child, Preschool , Female , Genetic Variation , Humans , Infant , Kenya/epidemiology , Male , Middle Aged , Pandemics , Phylogeny , Public Health , SARS-CoV-2/classification , SARS-CoV-2/isolation & purification , Sequence Analysis , Tanzania , Travel , Young Adult
18.
Glob Health Action ; 14(1): 1947565, 2021 01 01.
Article in English | MEDLINE | ID: covidwho-1331518

ABSTRACT

Essential health, education and other service disruptions arising from the COVID-19 pandemic risk reversing some of the hard-won gains in improving child survival over the past 40 years. Although children have milder symptoms of COVID-19 disease than adults, pandemic control measures in many countries have disrupted health, education and other services for children, often leaving them without access to birth and postnatal care, vaccinations and early childhood preventive and treatment services. These disruptions mean that the SARS-CoV-2 virus, along with climate change and shifting epidemiological and demographic patterns, are challenging the survival gains that we have seen over the past 40 years. We revisit the initiatives and actions of the past that catalyzed survival improvements in an effort to learn how to maintain these gains even in the face of today's global challenges.


Subject(s)
COVID-19 , Pandemics , Adult , Child , Child Health , Child, Preschool , Humans , SARS-CoV-2 , Vaccination
19.
Nat Commun ; 12(1): 3966, 2021 06 25.
Article in English | MEDLINE | ID: covidwho-1281718

ABSTRACT

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.


Subject(s)
Antibodies, Viral/immunology , COVID-19/diagnosis , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Adolescent , Adult , Antibodies, Viral/blood , Bayes Theorem , COVID-19/epidemiology , COVID-19/virology , Enzyme-Linked Immunosorbent Assay , Epidemics , Female , Humans , Immunoglobulin G/blood , Immunoglobulin G/immunology , Kenya/epidemiology , Male , Middle Aged , Prevalence , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Seroepidemiologic Studies , Spike Glycoprotein, Coronavirus/metabolism , Young Adult
20.
BMJ Glob Health ; 6(3)2021 03.
Article in English | MEDLINE | ID: covidwho-1148159

ABSTRACT

We have worked to develop a Clinical Information Network (CIN) in Kenya as an early form of learning health systems (LHS) focused on paediatric and neonatal care that now spans 22 hospitals. CIN's aim was to examine important outcomes of hospitalisation at scale, identify and ultimately solve practical problems of service delivery, drive improvements in quality and test interventions. By including multiple routine settings in research, we aimed to promote generalisability of findings and demonstrate potential efficiencies derived from LHS. We illustrate the nature and range of research CIN has supported over the past 7 years as a form of LHS. Clinically, this has largely focused on common, serious paediatric illnesses such as pneumonia, malaria and diarrhoea with dehydration with recent extensions to neonatal illnesses. CIN also enables examination of the quality of care, for example that provided to children with severe malnutrition and the challenges encountered in routine settings in adopting simple technologies (pulse oximetry) and more advanced diagnostics (eg, Xpert MTB/RIF). Although regular feedback to hospitals has been associated with some improvements in quality data continue to highlight system challenges that undermine provision of basic, quality care (eg, poor access to blood glucose testing and routine microbiology). These challenges include those associated with increased mortality risk (eg, delays in blood transfusion). Using the same data the CIN platform has enabled conduct of randomised trials and supports malaria vaccine and most recently COVID-19 surveillance. Employing LHS principles has meant engaging front-line workers, clinical managers and national stakeholders throughout. Our experience suggests LHS can be developed in low and middle-income countries that efficiently enable contextually appropriate research and contribute to strengthening of health services and research systems.


Subject(s)
Child Health Services/standards , Delivery of Health Care/standards , Health Services Accessibility/standards , Health Services Research , Quality Improvement , COVID-19/epidemiology , COVID-19/prevention & control , Child , Child, Preschool , Developing Countries , Diarrhea/epidemiology , Diarrhea/prevention & control , Humans , Infant , Infant, Newborn , Kenya/epidemiology , Malaria/epidemiology , Malaria/prevention & control , Pandemics , Pneumonia/epidemiology , Pneumonia/prevention & control , SARS-CoV-2
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