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Cholera continues to cause many outbreaks in low and middle-income countries due to inadequate water, sanitation, and hygiene services. We describe a protracted cholera outbreak in Nairobi City County, Kenya in 2017. We reviewed the cholera outbreak line lists from Nairobi City County in 2017 to determine its extent and factors associated with death. A suspected case of cholera was any person aged >2 years old who had acute watery diarrhea, nausea, or vomiting, whereas a confirmed case was where Vibrio cholerae was isolated from the stool specimen. We summarized cases using means for continuous variables and proportions for categorical variables. Associations between admission status, sex, age, residence, time to care seeking, and outbreak settings; and cholera associated deaths were assessed using odds ratio (OR) with 95% confidence interval (CI). Of the 2,737 cholera cases reported, we analyzed 2,347 (85.7%) cases including 1,364 (58.1%) outpatients, 1,724 (73.5%) not associated with mass gathering events, 1,356 (57.8%) male and 2,202 (93.8%) aged ≥5 years, and 35 deaths (case fatality rate: 1.5%). Cases were reported from all the Sub Counties of Nairobi City County with an overall county attack rate of 50 per 100,000 people. Vibrio cholerae Ogawa serotype was isolated from 78 (34.8%) of the 224 specimens tested and all isolates were sensitive to tetracycline and levofloxacin but resistant to amikacin. The odds of cholera-related deaths was lower among outpatient cases (aOR: 0.35; [95% CI: 0.17-0.72]), age ≥5 years old (aOR: 0.21 [95% CI: 0.09-0.55]), and mass gathering events (aOR: 0.26 [95% CI: 0.07-0.91]) while threefold higher odds among male (aOR: 3.04 [95% CI: 1.30-7.13]). Nairobi City County experienced a protracted and widespread cholera outbreak with a high case fatality rate in 2017.

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Acute febrile illness (AFI) is a common reason for healthcare seeking and hospitalization in Sub-Saharan Africa and is often presumed to be malaria. However, a broad range of pathogens cause fever, and more comprehensive data on AFI etiology can improve clinical management, prevent unnecessary prescriptions, and guide public health interventions. We conducted surveillance for AFI (temperature ≥38.0°C <14 days duration) among hospitalized patients of all ages at four sites in Kenya (Nairobi, Mombasa, Kakamega, and Kakuma). For cases of undifferentiated fever (UF), defined as AFI without diarrhea (≥3 loose stools in 24 hours) or lower respiratory tract symptoms (cough/difficulty breathing plus oxygen saturation <90% or [in children <5 years] chest indrawing), we tested venous blood with real-time PCR-based TaqMan array cards (TAC) for 17 viral, 8 bacterial, and 3 protozoal fever-causing pathogens. From June 2017 to March 2019, we enrolled 3,232 AFI cases; 2,529 (78.2%) were aged <5 years. Among 3,021 with outcome data, 131 (4.3%) cases died while in hospital, including 106/2,369 (4.5%) among those <5 years. Among 1,735 (53.7%) UF cases, blood was collected from 1,340 (77.2%) of which 1,314 (98.1%) were tested by TAC; 715 (54.4%) had no pathogens detected, including 147/196 (75.0%) of those aged <12 months. The most common pathogen detected was Plasmodium, as a single pathogen in 471 (35.8%) cases and in combination with other pathogens in 38 (2.9%). HIV was detected in 51 (3.8%) UF cases tested by TAC and was most common in adults (25/236 [10.6%] ages 18-49, 4/40 [10.0%] ages ≥50 years). Chikungunya virus was found in 30 (2.3%) UF cases, detected only in the Mombasa site. Malaria prevention and control efforts are critical for reducing the burden of AFI, and improved diagnostic testing is needed to provide better insight into non-malarial causes of fever. The high case fatality of AFI underscores the need to optimize diagnosis and appropriate management of AFI to the local epidemiology.

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BACKGROUND: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa. OBJECTIVE: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance. METHODS: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness. RESULTS: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82). CONCLUSIONS: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance.

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INTRODUCTION: In 2016, the Kenya National Immunization Technical Advisory Group requested additional programmatic and cost effectiveness data to inform the choice of strategy for a national influenza vaccination program among children aged 6-23 months of age. In response, we conducted an influenza vaccine demonstration project to compare the performance of a year-round versus campaign-mode vaccination strategy. Findings from this demonstration project will help identify essential learning lessons for a national program. METHODS: We compared two vaccine delivery strategies: (i) a year-round vaccination strategy where influenza vaccines were administered throughout the year at health facilities. This strategy was implemented in Njoro sub-county in Nakuru (November 2019 to October 2021) and Jomvu sub-county in Mombasa (December 2019 to October 2021), (ii) a campaign-mode vaccination strategy where vaccines were available at health facilities over four months. This strategy was implemented in Nakuru North sub-county in Nakuru (June to September 2021) and Likoni sub-county in Mombasa (July to October 2021). We assessed differences in coverage, dropout rates, vaccine wastage, and operational needs. RESULTS: We observed similar performance between strategies in coverage of the first dose of influenza vaccine (year-round strategy 59.7 %, campaign strategy 63.2 %). The coverage obtained in the year-round sub-counties was similar (Njoro 57.4 %; Jomvu 63.1 %); however, more marked differences between campaign sub-counties were observed (Nakuru North 73.4 %; Likoni 55.2 %). The campaign-mode strategy exceeded the cold chain capacity of participating health facilities, requiring thrice monthly instead of once monthly deliveries, and was associated with a two-fold increase in workload compared to the year-round strategy (168 vaccines administered per day in the campaign strategy versus 83 vaccines administered per day in the year-round strategy). CONCLUSION: Although both strategies had similar coverage levels, the campaign-mode strategy was associated with considerable operational needs that could significantly impact the immunization program.

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Cholera is an issue of major public health importance. It was first reported in Kenya in 1971, with the country experiencing outbreaks through the years, most recently in 2021. Factors associated with the outbreaks in Kenya include open defecation, population growth with inadequate expansion of safe drinking water and sanitation infrastructure, population movement from neighboring countries, crowded settings such as refugee camps coupled with massive displacement of persons, mass gathering events, and changes in rainfall patterns. The Ministry of Health, together with other ministries and partners, revised the national cholera control plan to a multisectoral cholera elimination plan that is aligned with the Global Roadmap for Ending Cholera. One of the key features in the revised plan is the identification of hotspots. The hotspot identification exercise followed guidance and tools provided by the Global Task Force on Cholera Control (GTFCC). Two epidemiological indicators were used to identify the sub-counties with the highest cholera burden: incidence per population and persistence. Additionally, two indicators were used to identify sub-counties with poor WASH coverage due to low proportions of households accessing improved water sources and improved sanitation facilities. The country reported over 25,000 cholera cases between 2015 and 2019. Of 290 sub-counties, 25 (8.6%) sub-counties were identified as a high epidemiological priority; 78 (26.9%) sub-counties were identified as high WASH priority; and 30 (10.3%) sub-counties were considered high priority based on a combination of epidemiological and WASH indicators. About 10% of the Kenyan population (4.89 million) is living in these 30-combination high-priority sub-counties. The novel method used to identify cholera hotspots in Kenya provides useful information to better target interventions in smaller geographical areas given resource constraints. Kenya plans to deploy oral cholera vaccines in addition to WASH interventions to the populations living in cholera hotspots as it targets cholera elimination by 2030.

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Background: Kenya detected the first case of COVID-19 on March 13, 2020, and as of July 30, 2020, 17 975 cases with 285 deaths (case fatality rate (CFR) = 1.6%) had been reported. This study described the cases during the early phase of the pandemic to provide information for monitoring and response planning in the local context. Methods: We reviewed COVID-19 case records from isolation centres while considering national representation and the WHO sampling guideline for clinical characterization of the COVID-19 pandemic within a country. Socio-demographic, clinical, and exposure data were summarized using median and mean for continuous variables and proportions for categorical variables. We assigned exposure variables to socio-demographics, exposure, and contact data, while the clinical spectrum was assigned outcome variables and their associations were assessed. Results: A total of 2796 case records were reviewed including 2049 (73.3%) male, 852 (30.5%) aged 30-39 years, 2730 (97.6%) Kenyans, 636 (22.7%) transporters, and 743 (26.6%) residents of Nairobi City County. Up to 609 (21.8%) cases had underlying medical conditions, including hypertension (n = 285 (46.8%)), diabetes (n = 211 (34.6%)), and multiple conditions (n = 129 (21.2%)). Out of 1893 (67.7%) cases with likely sources of exposure, 601 (31.8%) were due to international travel. There were 2340 contacts listed for 577 (20.6%) cases, with 632 contacts (27.0%) being traced. The odds of developing COVID-19 symptoms were higher among case who were aged above 60 years (odds ratio (OR) = 1.99, P = 0.007) or had underlying conditions (OR = 2.73, P < 0.001) and lower among transport sector employees (OR = 0.31, P < 0.001). The odds of developing severe COVID-19 disease were higher among cases who had underlying medical conditions (OR = 1.56, P < 0.001) and lower among cases exposed through community gatherings (OR = 0.27, P < 0.001). The odds of survival of cases from COVID-19 disease were higher among transport sector employees (OR = 3.35, P = 0.004); but lower among cases who were aged ≥60 years (OR = 0.58, P = 0.034) and those with underlying conditions (OR = 0.58, P = 0.025). Conclusion: The early phase of the COVID-19 pandemic demonstrated a need to target the elderly and comorbid cases with prevention and control strategies while closely monitoring asymptomatic cases.

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