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1.
PLoS One ; 17(10): e0265478, 2022.
Article in English | MEDLINE | ID: covidwho-2079676

ABSTRACT

INTRODUCTION: The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity. METHODS: We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection. RESULTS: We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi. CONCLUSIONS: There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.


Subject(s)
COVID-19 , Pregnancy Complications, Infectious , Antibodies, Viral , COVID-19/epidemiology , Cross-Sectional Studies , Female , Hospitals , Humans , Immunoglobulin G , Kenya/epidemiology , Pregnancy , Prenatal Care , Referral and Consultation , SARS-CoV-2 , Seroepidemiologic Studies , Spike Glycoprotein, Coronavirus
2.
Wellcome open research ; 6, 2021.
Article in English | EuropePMC | ID: covidwho-2046342

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.

4.
Int J Public Health ; 67: 1604854, 2022.
Article in English | MEDLINE | ID: covidwho-1933949

ABSTRACT

Objective: To estimate the economic impact of border closure and social distancing by estimating the decline of gross domestic product (GDP) in Kenya, Singapore and Thailand. Methods: We analysed secondary data retrospectively. To calculate impact of NPIs on GDP, the relationship between GDP and stock market index was examined using ordinary least squares (OLS). Then, autoregressive and moving averages (ARMA) model was used to examine the impact of NPI on stock market index. The change in GDP due to NPIs was derived by multiplying coefficients of OLS and ARMA models. Results: An increase in stock market index correlated with an increase in GDP, while both social distancing and border closure negatively correlated with stock market index. Implementation of NPIs correlated with the decline in GDP. Thai border closure had a greater decline in GDP than social distancing; Kenya exhibited the same trends; Singapore had the opposite trend. Conclusion: We quantified the magnitude of economic impact of NPIs in terms of GDP decline by linking stock market index and GDP. This approach may be applicable in other settings.


Subject(s)
Retrospective Studies , Humans , Kenya , Singapore , Thailand
5.
BMJ Open ; 12(6): e058688, 2022 06 28.
Article in English | MEDLINE | ID: covidwho-1909757

ABSTRACT

OBJECTIVES: To assess experiences of and response to the COVID-19 pandemic at community pharmacies in Kenya. DESIGN, SETTING AND PARTICIPANTS: This was a mixed-methods study conducted from November 2020 to April 2021, targeting service providers in three counties (Nairobi, Mombasa and Kisumu), selected purposively to represent the main urban centres; pharmacies were selected randomly from a list of licensed pharmacies. RESULTS: Of 195 sampled pharmacies, 108 (55%) completed a questionnaire and 103 (53%) received a simulated client call; 18 service providers were interviewed. The initial weeks of the pandemic were characterised by fear and panic among service providers and a surge in client flow. Subsequently, 65 (60%) of 108 pharmacies experienced a dip in demand to below prepandemic levels and 34 (31%) reported challenges with unavailability, high price and poor quality of products. Almost all pharmacies were actively providing preventive materials and therapies; educating clients on prevention measures; counselling anxious clients; and handling and referring suspect cases. Fifty-nine pharmacies (55% (95% CI 45% to 65%)) reported receiving a client asking for COVID-19 testing and a similar proportion stated they would support pharmacy-based testing if implemented. For treatment of simulated clients, most pharmacies (71%, 73 of 103) recommended alternative therapies and nutritional supplements such as vitamin C; the rest recommended conventional therapies such as antibiotics. While 52 (48%) of 108 pharmacies had at least one staff member trained on COVID-19, a general feeling of disconnection from the national programme prevailed. CONCLUSIONS: Private pharmacies in Kenya were actively contributing to the COVID-19 response, but more deliberate engagement, support and linkages are required. Notably, there is an urgent need to develop guidelines for pharmacy-based COVID-19 testing, a service that is clearly needed and which could greatly increase test coverage. Pharmacy-based COVID-19 programmes should be accompanied with implementation research to inform current and future pandemic responses.


Subject(s)
COVID-19 , Pharmacies , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Testing , Humans , Kenya/epidemiology , Pandemics/prevention & control
6.
Elife ; 112022 06 14.
Article in English | MEDLINE | ID: covidwho-1893302

ABSTRACT

Background: Detailed understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) regional transmission networks within sub-Saharan Africa is key for guiding local public health interventions against the pandemic. Methods: Here, we analysed 1139 SARS-CoV-2 genomes from positive samples collected between March 2020 and February 2021 across six counties of Coastal Kenya (Mombasa, Kilifi, Taita Taveta, Kwale, Tana River, and Lamu) to infer virus introductions and local transmission patterns during the first two waves of infections. Virus importations were inferred using ancestral state reconstruction, and virus dispersal between counties was estimated using discrete phylogeographic analysis. Results: During Wave 1, 23 distinct Pango lineages were detected across the six counties, while during Wave 2, 29 lineages were detected; 9 of which occurred in both waves and 4 seemed to be Kenya specific (B.1.530, B.1.549, B.1.596.1, and N.8). Most of the sequenced infections belonged to lineage B.1 (n = 723, 63%), which predominated in both Wave 1 (73%, followed by lineages N.8 [6%] and B.1.1 [6%]) and Wave 2 (56%, followed by lineages B.1.549 [21%] and B.1.530 [5%]). Over the study period, we estimated 280 SARS-CoV-2 virus importations into Coastal Kenya. Mombasa City, a vital tourist and commercial centre for the region, was a major route for virus imports, most of which occurred during Wave 1, when many Coronavirus Disease 2019 (COVID-19) government restrictions were still in force. In Wave 2, inter-county transmission predominated, resulting in the emergence of local transmission chains and diversity. Conclusions: Our analysis supports moving COVID-19 control strategies in the region from a focus on international travel to strategies that will reduce local transmission. Funding: This work was funded by The Wellcome (grant numbers: 220985, 203077/Z/16/Z, 220977/Z/20/Z, and 222574/Z/21/Z) and the National Institute for Health and Care 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. The views expressed in this publication are those of the author(s) and not necessarily those of the funding agencies.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Genomics , Humans , Kenya/epidemiology , Phylogeny , Retrospective Studies , SARS-CoV-2/genetics
7.
BMJ Open ; 12(6): e059501, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-1874560

ABSTRACT

OBJECTIVES: Researchers at the KEMRI-Wellcome Trust Research Programme (KWTRP) carried out knowledge translation (KT) activities to support policy-makers as the Kenyan Government responded to the COVID-19 pandemic. We assessed the usefulness of these activities to identify the facilitators and barriers to KT and suggest actions that facilitate KT in similar settings. DESIGN: The study adopted a qualitative interview study design. SETTING AND PARTICIPANTS: Researchers at KWTRP in Kenya who were involved in KT activities during the COVID-19 pandemic (n=6) were selected to participate in key informant interviews to describe their experience. In addition, the policy-makers with whom these researchers engaged were invited to participate (n=11). Data were collected from March 2021 to August 2021. ANALYSIS: A thematic analysis approach was adopted using a predetermined framework to develop a coding structure consisting of the core thematic areas. Any other theme that emerged in the coding process was included. RESULTS: Both groups reported that the KT activities increased evidence availability and accessibility, enhanced policy-makers' motivation to use evidence, improved capacity to use research evidence and strengthened relationships. Policy-makers shared that a key facilitator of this was the knowledge products shared and the regular interaction with researchers. Both groups mentioned that a key barrier was the timeliness of generating evidence, which was exacerbated by the pandemic. They felt it was important to institutionalise KT to improve readiness to respond to public health emergencies. CONCLUSION: This study provides a real-world example of the use of KT during a public health crisis. It further highlights the need to institutionalise KT in research and policy institutions in African countries to respond readily to public health emergencies.


Subject(s)
COVID-19 , Emergencies , Humans , Kenya , Pandemics , Policy , Qualitative Research , Translational Science, Biomedical
8.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336035

ABSTRACT

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.

9.
Wellcome Open Res ; 7: 69, 2022.
Article in English | MEDLINE | ID: covidwho-1835904

ABSTRACT

Background: There are limited studies in Africa describing the epidemiology, clinical characteristics and serostatus of individuals tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We tested routine samples from the Coastal part of Kenya between 17 th March 2020 and 30 th June 2021. Methods: SARS-CoV-2 infections identified using reverse transcription polymerase chain reaction (RT-PCR) and clinical surveillance data at the point of sample collection were used to classify as either symptomatic or asymptomatic. IgG antibodies were measured in sera samples, using a well validated in-house enzyme-linked immunosorbent assay (ELISA). Results: Mombasa accounted for 56.2% of all the 99,694 naso-pharyngeal/oro-pharyngeal swabs tested, and males constituted the majority tested (73.4%). A total of 7737 (7.7%) individuals were SARS-CoV-2 positive by RT-PCR. The majority (i.e., 92.4%) of the RT-PCR positive individuals were asymptomatic. Testing was dominated by mass screening and travellers, and even at health facility level 91.6% of tests were from individuals without symptoms. Out of the 97,124 tests from asymptomatic individuals 7,149 (7%) were positive and of the 2,568 symptomatic individuals 588 (23%) were positive. In total, 2458 serum samples were submitted with paired naso-pharyngeal/oro-pharyngeal samples and 45% of the RT-PCR positive samples and 20% of the RT-PCR negative samples were paired with positive serum samples. Symptomatic individuals had significantly higher antibody levels than asymptomatic individuals and become RT-PCR negative on repeat testing earlier than asymptomatic individuals. Conclusions: In conclusion, the majority of SARS-CoV-2 infections identified by routine testing in Coastal Kenya were asymptomatic. This reflects the testing practice of health services in Kenya, but also implies that asymptomatic infection is very common in the population. Symptomatic infection may be less common, or it may be that individuals do not present for testing when they have symptoms.

10.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-335508

ABSTRACT

Essential Emergency and Critical Care (EECC) is a novel approach to the care of critically ill patients, focusing on first-tier, low-cost care and designed to be feasible even in low-resourced and low-staffed settings. This is distinct from advanced critical care, usually conducted in ICUs with specialised staff, facilities and technologies. This paper estimates the incremental cost of EECC and advanced critical care for the planning of care for critically ill patients in low resource settings with Kenya and Tanzania as case studies.The incremental costing took a health systems perspective. A normative approach based on the ingredients defined through the recently published global consensus on EECC was used. The setting was a district hospital in which the patient is provided with the definitive care typically provided at that level for their condition. Quantification of resource use was based on COVID-19 as a tracer condition using clinical expertise. Local prices were used where available, and all costs were converted to USD2020.The costs per patient day of EECC is estimated to be 1.01 USD, 10.83 USD and 32.84 USD in Tanzania and 1.76 USD, 14.86 USD and 37.43 USD in Kenya, for moderate, severe and critical COVID-19 patients respectively. The cost per patient day of advanced critical care is estimated to be 13.11 USD and 17.33 USD for severe and 297.30 USD and 369.64 USD for critical COVID-19 patients in Tanzania and Kenya, respectively.EECC, an approach of providing the essential care to all critically ill patients, is low-cost. The components of EECC are basic and universal and, when assessed against the existing gaps in critical care coverage and costs of advanced critical care, suggest that it should be a priority area of investment for health systems around the globe.

11.
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.

12.
BMC Health Serv Res ; 22(1): 439, 2022 Apr 04.
Article in English | MEDLINE | ID: covidwho-1775322

ABSTRACT

BACKGROUND: Vaccines are considered the path out of the COVID-19 pandemic. The government of Kenya is implementing a phased strategy to vaccinate the Kenyan population, initially targeting populations at high risk of severe disease and infection. We estimated the financial and economic unit costs of procuring and delivering the COVID-19 vaccine in Kenya across various vaccination strategies. METHODS: We used an activity-based costing approach to estimate the incremental costs of COVID-19 vaccine delivery, from a health systems perspective. Document reviews and key informant interviews(n = 12) were done to inform the activities, assumptions and the resources required. Unit prices were derived from document reviews or from market prices. Both financial and economic vaccine procurement costs per person vaccinated with 2-doses, and the vaccine delivery costs per person vaccinated with 2-doses were estimated and reported in 2021USD. RESULTS: The financial costs of vaccine procurement per person vaccinated with 2-doses ranged from $2.89-$13.09 in the 30% and 100% coverage levels respectively, however, the economic cost was $17.34 across all strategies. Financial vaccine delivery costs per person vaccinated with 2-doses, ranged from $4.28-$3.29 in the 30% and 100% coverage strategies: While the economic delivery costs were two to three times higher than the financial costs. The total procurement and delivery costs per person vaccinated with 2-doses ranged from $7.34-$16.47 for the financial costs and $29.7-$24.68 for the economic costs for the 30% and 100% coverage respectively. With the exception of procurement costs, the main cost driver of financial and economic delivery costs was supply chain costs (47-59%) and advocacy, communication and social mobilization (29-35%) respectively. CONCLUSION: This analysis presents cost estimates that can be used to inform local policy and may further inform parameters used in cost-effectiveness models. The results could potentially be adapted and adjusted to country-specific assumptions to enhance applicability in similar low-and middle-income settings.


Subject(s)
COVID-19 Vaccines , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Humans , Immunization Programs , Kenya/epidemiology , Pandemics
13.
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.

14.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329220

ABSTRACT

Background: There are limited studies in Africa describing the epidemiology, clinical characteristics and serostatus of individuals tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We tested routine samples from the Coastal part of Kenya between 17 th March 2020 and 30 th June 2021. Methods: : SARS-CoV-2 infections identified using reverse transcription polymerase chain reaction (RT-PCR) and clinical surveillance data at the point of sample collection were used to classify as either symptomatic or asymptomatic. IgG antibodies were measured in sera samples, using a well validated in-house enzyme-linked immunosorbent assay (ELISA). Results: : Mombasa accounted for 56.2% of all the 99,694 naso-pharyngeal/oro-pharyngeal swabs tested, and males constituted the majority tested (73.4%). A total of 7737 (7.7%) individuals were SARS-CoV-2 positive by RT-PCR. The majority (i.e., 92.4%) of the RT-PCR positive individuals were asymptomatic. Testing was dominated by mass screening and travellers, and even at health facility level 91.6% of tests were from individuals without symptoms. Out of the 97,124 tests from asymptomatic individuals 7,149 (7%) were positive and of the 2,568 symptomatic individuals 588 (23%) were positive. In total, 2458 serum samples were submitted with paired naso-pharyngeal/oro-pharyngeal samples and 45% of the RT-PCR positive samples and 20% of the RT-PCR negative samples were paired with positive serum samples. Symptomatic individuals had significantly higher antibody levels than asymptomatic individuals and become RT-PCR negative on repeat testing earlier than asymptomatic individuals. Conclusions: : In conclusion, the majority of SARS-CoV-2 infections identified by routine testing in Coastal Kenya were asymptomatic. This reflects the testing practice of health services in Kenya, but also implies that asymptomatic infection is very common in the population. Symptomatic infection may be less common, or it may be that individuals do not present for testing when they have symptoms.

15.
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.

16.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323474

ABSTRACT

Background: Across the African continent, other than South Africa, COVID-19 cases have remained relatively low. Nevertheless, in Kenya, despite early implementation of containment measures and restrictions, cases have consistently been increasing. Contact tracing forms one of the key strategies in Kenya, but may become infeasible as the caseload grows. Here we explore different contact tracing strategies by distinguishing between household and non-household contacts and how these may be combined with other non-pharmaceutical interventions. Methods: We extend a previously developed branching process model for contact tracing to include realistic contact data from Kenya. Using the contact data, we generate a synthetic population of individuals and their contacts categorised by age and household membership. We simulate the initial spread of SARS-CoV-2 through this population and look at the effectiveness of a number of non-pharmaceutical interventions with a particular focus on different contact tracing strategies and the potential effort involved in these. Results: General physical distancing and avoiding large group gatherings combined with contact tracing, where all contacts are isolated immediately, can be effective in slowing down the outbreak, but were, under our base assumptions, not enough to control it without implementing extreme stay at home policies. Under optimistic assumptions with a highly overdispersed R0 and a short delay from symptom onset to isolation, control was possible with less stringent physical distancing and by isolating household contacts only. Conclusions: Without strong physical distancing measures, controlling the spread of SARS-CoV-2 is difficult. With limited resources, physical distancing combined with the isolation of households of detected cases can form a moderately effective strategy, and control is possible under optimistic assumptions. More data are needed to understand transmission in Kenya, in particular by studying the settings that lead to larger transmission events, which may allow for more targeted responses, and collection of representative age-related contact data.

17.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-323243

ABSTRACT

Covid-19 requires policy makers to consider evidence on both population health and economic welfare. Over the last two decades, the field of health economics has developed a range of analytical approaches and contributed to the institutionalisation of processes to employ economic evidence in health policy. We present a discussion outlining how these approaches and processes need to be applied more widely to inform Covid-19 policy;highlighting where they may need to be adapted conceptually and methodologically, and providing examples of work to date. We focus on the evidential and policy needs of low- and middle-income countries;where there is an urgent need for evidence to navigate the policy trade-offs between health and economic well-being posed by the Covid-19 pandemic.

18.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-318481

ABSTRACT

Background: COVID-19 mitigation measures have major ramifications on all aspects of people’s livelihoods. Based on data collected in February 2021, we present an analysis of the socio-economic impacts of COVID-19 mitigation measures in three counties in Kenya. Methods: : We conducted a cross-sectional phone-based survey in three counties in Kenya to assess the level of disruption across seven domains: income, food insecurity, schooling, domestic tension/violence, communal violence, mental health, and decision-making. An overall disruption index was computed from the seven domains using principal component analysis. We used a linear regression model to examine the determinants of vulnerability to disruptions as measured by the index. We used concentration curves and indices to assess inequality in the disruption domains and the overall disruption index. Results: : The level of disruption in income was the highest (74%), while the level of disruption for domestic tension/violence was the lowest (30%). Factors associated with increased vulnerability to the overall disruption index included: older age, being married, belonging in the lowest socio-economic tertile and receiving COVID-19 related assistance. The concentration curves showed that all the seven domains of disruption were disproportionately concentrated among households in the lowest socio-economic tertile, a finding that was supported by the concentration index of the overall disruption index (CI = - 0.022;p = 0.074). Conclusion: The COVID-19 mitigation measures resulted in unintended socio-economic effects that unfairly affected certain vulnerable groups, including those in the lowest socio-economic group and the elderly. Measures to protect households against the adverse socio-economic effects of the pandemic should be scaled up and targeted to the most vulnerable, with attention to the constantly evolving nature of the pandemic.

19.
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.

20.
PLoS One ; 17(2): e0261904, 2022.
Article in English | MEDLINE | ID: covidwho-1674004

ABSTRACT

The need for resilient health systems is recognized as important for the attainment of health outcomes, given the current shocks to health services. Resilience has been defined as the capacity to "prepare and effectively respond to crises; maintain core functions; and, informed by lessons learnt, reorganize if conditions require it". There is however a recognized dichotomy between its conceptualization in literature, and its application in practice. We propose two mutually reinforcing categories of resilience, representing resilience targeted at potentially known shocks, and the inherent health system resilience, needed to respond to unpredictable shock events. We determined capacities for each of these categories, and explored this methodological proposition by computing country-specific scores against each capacity, for the 47 Member States of the WHO African Region. We assessed face validity of the computed index, to ensure derived values were representative of the different elements of resilience, and were predictive of health outcomes, and computed bias-corrected non-parametric confidence intervals of the emergency preparedness and response (EPR) and inherent system resilience (ISR) sub-indices, as well as the overall resilience index, using 1000 bootstrap replicates. We also explored the internal consistency and scale reliability of the index, by calculating Cronbach alphas for the various proposed capacities and their corresponding attributes. We computed overall resilience to be 48.4 out of a possible 100 in the 47 assessed countries, with generally lower levels of ISR. For ISR, the capacities were weakest for transformation capacity, followed by mobilization of resources, awareness of own capacities, self-regulation and finally diversity of services respectively. This paper aims to contribute to the growing body of empirical evidence on health systems and service resilience, which is of great importance to the functionality and performance of health systems, particularly in the context of COVID-19. It provides a methodological reflection for monitoring health system resilience, revealing areas of improvement in the provision of essential health services during shock events, and builds a case for the need for mechanisms, at country level, that address both specific and non-specific shocks to the health system, ultimately for the attainment of improved health outcomes.


Subject(s)
COVID-19/prevention & control , Delivery of Health Care/standards , Disaster Planning/methods , Health Resources/statistics & numerical data , Health Services Needs and Demand , Medical Assistance/standards , Resilience, Psychological , Africa/epidemiology , COVID-19/epidemiology , COVID-19/transmission , COVID-19/virology , Humans , Reproducibility of Results , SARS-CoV-2/isolation & purification , World Health Organization
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