An epidemiological analysis of Acute Flaccid Paralysis (AFP) surveillance in Kenya, 2016 to 2018.

BACKGROUND: The poliovirus has been targeted for eradication since 1988. Kenya reported its last case of indigenous Wild Poliovirus (WPV) in 1984 but suffered from an outbreak of circulating Vaccine-derived Poliovirus type 2 (cVDPV2) in 2018. We aimed to describe Kenya's polio surveillance performance 2016-2018 using WHO recommended polio surveillance standards. METHODS: Retrospective secondary data analysis was conducted using Kenyan AFP surveillance case-based database from 2016 to 2018. Analyses were carried out using Epi-Info statistical software (version 7) and mapping was done using Quantum Geographic Information System (GIS) (version 3.4.1). RESULTS: Kenya reported 1706 cases of AFP from 2016 to 2018. None of the cases were confirmed as poliomyelitis. However, 23 (1.35%) were classified as polio compatible. Children under 5 years accounted for 1085 (63.6%) cases, 937 (55.0%) cases were boys, and 1503 (88.1%) cases had received three or more doses of Oral Polio Vaccine (OPV). AFP detection rate substantially increased over the years; however, the prolonged health workers strike in 2017 negatively affected key surveillance activities. The mean Non-Polio (NP-AFP) rate during the study period was 2.87/ 100,000 children under 15 years, and two adequate specimens were collected for 1512 (88.6%) AFP cases. Cumulatively, 31 (66.0%) counties surpassed target for both WHO recommended AFP quality indicators. CONCLUSIONS: The performance of Kenya's AFP surveillance system surpassed the minimum WHO recommended targets for both non-polio AFP rate and stool adequacy during the period studied. In order to strengthen the country's polio free status, health worker's awareness on AFP surveillance and active case search should be strengthened in least performing counties to improve case detection. Similar analyses should be done at the sub-county level to uncover underperformance that might have been hidden by county level analysis.

Use of technology for public health surveillance reporting: opportunities, challenges and lessons learnt from Kenya.

BACKGROUND: Effective public health surveillance systems are crucial for early detection and response to outbreaks. In 2016, Kenya transitioned its surveillance system from a standalone web-based surveillance system to the more sustainable and integrated District Health Information System 2 (DHIS2). As part of Global Health Security Agenda (GHSA) initiatives in Kenya, training on use of the new system was conducted among surveillance officers. We evaluated the surveillance indicators during the transition period in order to assess the impact of this training on surveillance metrics and identify challenges affecting reporting rates. METHODS: From February to May 2017, we analysed surveillance data for 13 intervention and 13 comparison counties. An intervention county was defined as one that had received refresher training on DHIS2 while a comparison county was one that had not received training. We evaluated the impact of the training by analysing completeness and timeliness of reporting 15 weeks before and 12 weeks after the training. A chi-square test of independence was used to compare the reporting rates between the two groups. A structured questionnaire was administered to the training participants to assess the challenges affecting surveillance reporting. RESULTS: The average completeness of reporting for the intervention counties increased from 45 to 62%, i.e. by 17 percentage points (95% CI 16.14-17.86) compared to an increase from 49 to 52% for the comparison group, i.e. by 3 percentage points (95% CI 2.23-3.77). The timeliness of reporting increased from 30 to 51%, i.e. by 21 percentage points (95% CI 20.16-21.84) for the intervention group, compared to an increase from 31 to 38% for the comparison group, i.e.by 7 percentage points (95% CI 6.27-7.73). Major challenges for the low reporting rates included lack of budget support from government, lack of airtime for reporting, health workers strike, health facilities not sending surveillance data, use of wrong denominator to calculate reporting rates and surveillance officers having other competing tasks. CONCLUSIONS: Training plays an important role in improving public health surveillance reporting. However, to improve surveillance reporting rates to the desired national targets, other challenges affecting reporting must be identified and addressed accordingly.

Characterization of Vibrio cholerae O1 isolates responsible for cholera outbreaks in Kenya between 1975 and 2017.

Kenya is endemic for cholera with different waves of outbreaks having been documented since 1971. In recent years, new variants of Vibrio cholerae O1 have emerged and have replaced most of the traditional El Tor biotype globally. These strains also appear to have increased virulence, and it is important to describe and document their phenotypic and genotypic traits. This study characterized 146 V. cholerae O1 isolates from cholera outbreaks that occurred in Kenya between 1975 and 2017. Our study reports that the 1975-1984 strains had typical classical or El Tor biotype characters. New variants of V. cholerae O1 having traits of both classical and El Tor biotypes were observed from 2007 with all strains isolated between 2015 and 2017 being sensitive to polymyxin B and carrying both classical and El Tor type ctxB. All strains were resistant to Phage IV and harbored rstR, rtxC, hlyA, rtxA and tcpA genes specific for El Tor biotype indicating that the strains had an El Tor backbone. Pulsed field gel electrophoresis (PFGE) genotyping differentiated the isolates into 14 pulsotypes. The clustering also corresponded with the year of isolation signifying that the cholera outbreaks occurred as separate waves of different genetic fingerprints exhibiting different genotypic and phenotypic characteristics. The emergence and prevalence of V. cholerae O1 strains carrying El Tor type and classical type ctxB in Kenya are reported. These strains have replaced the typical El Tor biotype in Kenya and are potentially more virulent and easily transmitted within the population.

Systems, supplies, and staff: a mixed-methods study of health care workers' experiences and health facility preparedness during a large national cholera outbreak, Kenya 2015.

BACKGROUND: From December 2014 to September 2016, a cholera outbreak in Kenya, the largest since 2010, caused 16,840 reported cases and 256 deaths. The outbreak affected 30 of Kenya's 47 counties and occurred shortly after the decentralization of many healthcare services to the county level. This mixed-methods study, conducted June-July 2015, assessed cholera preparedness in Homa Bay, Nairobi, and Mombasa counties and explored clinic- and community-based health care workers' (HCW) experiences during outbreak response. METHODS: Counties were selected based on cumulative cholera burden and geographic characteristics. We conducted 44 health facility cholera preparedness checklists (according to national guidelines) and 8 focus group discussions (FGDs). Frequencies from preparedness checklists were generated. To determine key themes from FGDs, inductive and deductive codes were applied; MAX software for qualitative data analysis (MAXQDA) was used to identify patterns. RESULTS: Some facilities lacked key materials for treating cholera patients, diagnosing cases, and maintaining infection control. Overall, 82% (36/44) of health facilities had oral rehydration salts, 65% (28/43) had IV fluids, 27% (12/44) had rectal swabs, 11% (5/44) had Cary-Blair transport media, and 86% (38/44) had gloves. A considerable number of facilities lacked disease reporting forms (34%, 14/41) and cholera treatment guidelines (37%, 16/43). In FDGs, HCWs described confusion regarding roles and reporting during the outbreak, which highlighted issues in coordination and management structures within the health system. Similar to checklist findings, FGD participants described supply challenges affecting laboratory preparedness and infection prevention and control. Perceived successes included community engagement, health education, strong collaboration between clinic and community HCWs, and HCWs' personal passion to help others. CONCLUSIONS: The confusion over roles, reporting, and management found in this evaluation highlights a need to adapt, implement, and communicate health strategies at the county level, in order to inform and train HCWs during health system transformations. International, national, and county stakeholders could strengthen preparedness and response for cholera and other public health emergencies in Kenya, and thereby strengthen global health security, through further investment in the existing Integrated Disease Surveillance and Response structure and national cholera prevention and control plan, and the adoption of county-specific cholera control plans.

Detection of dengue virus serotypes 1, 2 and 3 in selected regions of Kenya: 2011-2014.

BACKGROUND: Dengue fever, a mosquito-borne disease, is associated with illness of varying severity in countries in the tropics and sub tropics. Dengue cases continue to be detected more frequently and its geographic range continues to expand. We report the largest documented laboratory confirmed circulation of dengue virus in parts of Kenya since 1982. METHODS: From September 2011 to December 2014, 868 samples from febrile patients were received from hospitals in Nairobi, northern and coastal Kenya. The immunoglobulin M enzyme linked immunosorbent assay (IgM ELISA) was used to test for the presence of IgM antibodies against dengue, yellow fever, West Nile and Zika. Reverse transcription polymerase chain reaction (RT-PCR) utilizing flavivirus family, yellow fever, West Nile, consensus and sero type dengue primers were used to detect acute arbovirus infections and determine the infecting serotypes. Representative samples of PCR positive samples for each of the three dengue serotypes detected were sequenced to confirm circulation of the various dengue serotypes. RESULTS: Forty percent (345/868) of the samples tested positive for dengue by either IgM ELISA (14.6 %) or by RT-PCR (25.1 %). Three dengue serotypes 1-3 (DENV1-3) were detected by serotype specific RT-PCR and sequencing with their numbers varying from year to year and by region. The overall predominant serotype detected from 2011-2014 was DENV1 accounting for 44 % (96/218) of all the serotypes detected, followed by DENV2 accounting for 38.5 % (84/218) and then DENV3 which accounted for 17.4 % (38/218). Yellow fever, West Nile and Zika was not detected in any of the samples tested. CONCLUSION: From 2011-2014 serotypes 1, 2 and 3 were detected in the Northern and Coastal parts of Kenya. This confirmed the occurrence of cases and active circulation of dengue in parts of Kenya. These results have documented three circulating serotypes and highlight the need for the establishment of active dengue surveillance to continuously detect cases, circulating serotypes, and determine dengue fever disease burden in the country and region.

Notes from the Field: Ongoing Cholera Outbreak - Kenya, 2014-2016.

On January 6, 2015, a man aged 40 years was admitted to Kenyatta National Hospital in Nairobi, Kenya, with acute watery diarrhea. The patient was found to be infected with toxigenic Vibrio cholerae serogroup O1, serotype Inaba. A subsequent review of surveillance reports identified four patients in Nairobi County during the preceding month who met either of the Kenya Ministry of Health suspected cholera case definitions: 1) severe dehydration or death from acute watery diarrhea (more than four episodes in 12 hours) in a patient aged ≥5 years, or 2) acute watery diarrhea in a patient aged ≥2 years in an area where there was an outbreak of cholera. An outbreak investigation was immediately initiated. A confirmed cholera case was defined as isolation of V. cholerae O1 or O139 from the stool of a patient with suspected cholera or a suspected cholera case that was epidemiologically linked to a confirmed case. By January 15, 2016, a total of 11,033 suspected or confirmed cases had been reported from 22 of Kenya's 47 counties (Table). The outbreak is ongoing.

Enteric pathogens and factors associated with acute bloody diarrhoea, Kenya.

BACKGROUND: Shigellosis is the major cause of bloody diarrhoea worldwide and is endemic in most developing countries. In Kenya, bloody diarrhoea is reported weekly as part of priority diseases under Integrated Disease Surveillance and Response System (IDSR) in the Ministry of Health. METHODS: We conducted a case control study with 805 participants (284 cases and 521 controls) between January and December 2012 in Kilifi and Nairobi Counties. Kilifi County is largely a rural population whereas Nairobi County is largely urban. A case was defined as a person of any age who presented to outpatient clinic with acute diarrhoea with visible blood in the stool in six selected health facilities in the two counties within the study period. A control was defined as a healthy person of similar age group and sex with the case and lived in the neighbourhood of the case. RESULTS: The main presenting clinical features for bloody diarrhoea cases were; abdominal pain (69 %), mucous in stool (61 %), abdominal discomfort (54 %) and anorexia (50 %). Pathogen isolation rate was 40.5 % with bacterial and protozoal pathogens accounting for 28.2 % and 12.3 % respectively. Shigella was the most prevalent bacterial pathogen isolated in 23.6 % of the cases while Entamoeba histolytica was the most prevalent protozoal pathogen isolated in 10.2 % of the cases. On binary logistic regression, three variables were found to be independently and significantly associated with acute bloody diarrhoea at 5 % significance level; storage of drinking water separate from water for other use (OR = 0.41, 95 % CI 0.20-0.87, p = 0.021), washing hands after last defecation (OR = 0.24, 95 % CI 0.08-.076, p = 0.015) and presence of coliforms in main source water (OR = 2.56, CI 1.21-5.4, p = 0.014). Rainfall and temperature had strong positive correlation with bloody diarrhoea. CONCLUSION: The main etiologic agents for bloody diarrhoea were Shigella and E. histolytica. Good personal hygiene practices such as washing hands after defecation and storing drinking water separate from water for other use were found to be the key protective factors for the disease while presence of coliform in main water source was found to be a risk factor. Implementation of water, sanitation and hygiene (WASH) interventions is therefore key in prevention and control of bloody diarrhoea.

A rapid assessment of drinking water quality in informal settlements after a cholera outbreak in Nairobi, Kenya.

Populations living in informal settlements with inadequate water and sanitation infrastructure are at risk of epidemic disease. In 2010, we conducted 398 household surveys in two informal settlements in Nairobi, Kenya with isolated cholera cases. We tested source and household water for free chlorine residual (FCR) and Escherichia coli in approximately 200 households. International guidelines are ≥0.5 mg/L FCR at source, ≥0.2 mg/L at household, and <1 E. coli/100 mL. In these two settlements, 82% and 38% of water sources met FCR guidelines; and 7% and 8% were contaminated with E. coli, respectively. In household stored water, 82% and 35% met FCR guidelines and 11% and 32% were contaminated with E. coli, respectively. Source water FCR≥0.5 mg/L (p=0.003) and reported purchase of a household water treatment product (p=0.002) were associated with increases in likelihood that household stored water had ≥0.2 mg/L FCR, which was associated with a lower likelihood of E. coli contamination (p<0.001). These results challenge the assumption that water quality in informal settlements is universally poor and the route of disease transmission, and highlight that providing centralized water with ≥0.5 mg/L FCR or (if not feasible) household water treatment technologies reduces the risk of waterborne cholera transmission in informal settlements.

National surveillance data on the epidemiology of cholera in Kenya, 1997-2010.

BACKGROUND: Kenya has experienced multiple cholera outbreaks since 1971. Cholera remains an issue of major public health importance and one of the 35 priority diseases under Kenya's updated Integrated Disease Surveillance and Response strategy. METHODS: We reviewed the cholera surveillance data reported to the World Health Organization and the Kenya Ministry of Public Health and Sanitation from 1997 through 2010 to determine trends in cholera disease for the 14-year period. RESULTS: A total of 68 522 clinically suspected cases of cholera and 2641 deaths were reported (overall case-fatality rate [CFR], 3.9%), affecting all regions of the country. Kenya's largest outbreak occurred during 1997-1999, resulting in 26 901 cases and 1362 deaths (CFR, 5.1%). Following a decline in disease occurrence, the country experienced a resurgence of epidemic cholera during 2007-2009 (16 616 cases and 454 deaths; CFR, 2.7%), which declined rapidly to 0 cases. Cases were reported through July 2010, with no cases reported during the second half of the year. About 42% of cases occurred in children aged <15 years. Vibrio cholerae O1, serotype Inaba, was the predominant strain recorded from 2007 through 2010, although serotype Ogawa was also isolated. Recurrent outbreaks have most frequently affected Nairobi, Nyanza, and Coast provinces, as well as remote arid and semiarid regions and refugee camps. DISCUSSION: Kenya has experienced substantial amounts of reported cases of cholera during the past 14 years. Recent decreases in cholera case counts may reflect cholera control measures put in place by the National Ministry of Health; confirmation of this theory will require ongoing surveillance.

A national cholera epidemic with high case fatality rates--Kenya 2009.

BACKGROUND: Cholera remains endemic in sub-Saharan Africa. We characterized the 2009 cholera outbreaks in Kenya and evaluated the response. METHODS: We analyzed surveillance data and estimated case fatality rates (CFRs). Households in 2 districts, East Pokot (224 cases; CFR = 11.7%) and Turkana South (1493 cases; CFR = 1.0%), were surveyed. We randomly selected 15 villages and 8 households per village in each district. Healthcare workers at 27 health facilities (HFs) were surveyed in both districts. RESULTS: In 2009, cholera outbreaks caused a reported 11 425 cases and 264 deaths in Kenya. Data were available from 44 districts for 6893 (60%) cases. District CFRs ranged from 0% to 14.3%. Surveyed household respondents (n = 240) were aware of cholera (97.5%) and oral rehydration solution (ORS) (87.9%). Cholera deaths were reported more frequently from East Pokot (n = 120) than Turkana South (n = 120) households (20.7% vs. 12.3%). The average travel time to a HF was 31 hours in East Pokot compared with 2 hours in Turkana South. Fewer respondents in East Pokot (9.8%) than in Turkana South (33.9%) stated that ORS was available in their village. ORS or intravenous fluid shortages occurred in 20 (76.9%) surveyed HFs. CONCLUSIONS: High CFRs in Kenya are related to healthcare access disparities, including availability of rehydration supplies.

A protracted cholera outbreak in Nairobi City County accentuated by mass gathering events, Kenya, 2017.

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.

Prioritizing interventions for cholera control in Kenya, 2015-2020.

Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015-2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasizes the need to target multi-sectoral interventions in priority cholera burden hotspots. This study utilizes the GTFCC's hotspot method to identify hotspots in Kenya at the county and sub-county administrative levels from 2015 through 2020. 32 of 47 (68.1%) counties reported cholera cases during this time while only 149 of 301 (49.5%) sub-counties reported cholera cases. The analysis identifies hotspots based on the mean annual incidence (MAI) over the past five-year period and cholera's persistence in the area. Applying a MAI threshold of 90th percentile and the median persistence at both the county and sub-county levels, we identified 13 high risk sub-counties from 8 counties, including the 3 high risk counties of Garissa, Tana River and Wajir. This demonstrates that several sub-counties are high level hotspots while their counties are not. In addition, when cases reported by county versus sub-county hotspot risk are compared, 1.4 million people overlapped in the areas identified as both high-risk county and high-risk sub-county. However, assuming that finer scale data is more accurate, 1.6 million high risk sub-county people would have been misclassified as medium risk with a county-level analysis. Furthermore, an additional 1.6 million people would have been classified as living in high-risk in a county-level analysis when at the sub-county level, they were medium, low or no-risk sub-counties. This results in 3.2 million people being misclassified when county level analysis is utilized rather than a more-focused sub-county level analysis. This analysis highlights the need for more localized risk analyses to target cholera intervention and prevention efforts towards the populations most vulnerable.

Mapping of cholera hotspots in Kenya using epidemiologic and water, sanitation, and hygiene (WASH) indicators as part of Kenya's new 2022-2030 cholera elimination plan.

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.

Outbreak of Middle East Respiratory Syndrome Coronavirus in Camels and Probable Spillover Infection to Humans in Kenya.

The majority of Kenya's > 3 million camels have antibodies against Middle East respiratory syndrome coronavirus (MERS-CoV), although human infection in Africa is rare. We enrolled 243 camels aged 0−24 months from 33 homesteads in Northern Kenya and followed them between April 2018 to March 2020. We collected and tested camel nasal swabs for MERS-CoV RNA by RT-PCR followed by virus isolation and whole genome sequencing of positive samples. We also documented illnesses (respiratory or other) among the camels. Human camel handlers were also swabbed, screened for respiratory signs, and samples were tested for MERS-CoV by RT-PCR. We recorded 68 illnesses among 58 camels, of which 76.5% (52/68) were respiratory signs and the majority of illnesses (73.5% or 50/68) were recorded in 2019. Overall, 124/4692 (2.6%) camel swabs collected from 83 (34.2%) calves in 15 (45.5%) homesteads between April−September 2019 screened positive, while 22 calves (26.5%) recorded reinfections (second positive swab following ≥ 2 consecutive negative tests). Sequencing revealed a distinct Clade C2 virus that lacked the signature ORF4b deletions of other Clade C viruses. Three previously reported human PCR positive cases clustered with the camel infections in time and place, strongly suggesting sporadic transmission to humans during intense camel outbreaks in Northern Kenya.

Characterization of COVID-19 cases in the early phase (March to July 2020) of the pandemic in Kenya.

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.

Impact of Kenya's Frontline Epidemiology Training Program on Outbreak Detection and Surveillance Reporting: A Geographical Assessment, 2014-2017.

Rapid detection and response to infectious disease outbreaks requires a robust surveillance system with a sufficient number of trained public health workforce personnel. The Frontline Field Epidemiology Training Program (Frontline) is a focused 3-month program targeting local ministries of health to strengthen local disease surveillance and reporting capacities. Limited literature exists on the impact of Frontline graduates on disease surveillance completeness and timeliness reporting. Using routinely collected Ministry of Health data, we mapped the distribution of graduates between 2014 and 2017 across 47 Kenyan counties. Completeness was defined as the proportion of complete reports received from health facilities in a county compared with the total number of health facilities in that county. Timeliness was defined as the proportion of health facilities submitting surveillance reports on time to the county. Using a panel analysis and controlling for county-fixed effects, we evaluated the relationship between the number of Frontline graduates and priority disease reporting of measles. We found that Frontline training was correlated with improved completeness and timeliness of weekly reporting for priority diseases. The number of Frontline graduates increased by 700%, from 57 graduates in 2014 to 456 graduates in 2017. The annual average rates of reporting completeness increased from 0.8% in 2014 to 55.1% in 2017. The annual average timeliness reporting rates increased from 0.1% in 2014 to 40.5% in 2017. These findings demonstrate how global health security implementation progress in workforce development may influence surveillance and disease reporting.

A protracted cholera outbreak among residents in an urban setting, Nairobi county, Kenya, 2015.

INTRODUCTION: in 2015, a cholera outbreak was confirmed in Nairobi county, Kenya, which we investigated to identify risk factors for infection and recommend control measures. METHODS: we analyzed national cholera surveillance data to describe epidemiological patterns and carried out a case-control study to find reasons for the Nairobi county outbreak. Suspected cholera cases were Nairobi residents aged >2 years with acute watery diarrhea (>4 stools/≤12 hours) and illness onset 1-14 May 2015. Confirmed cases had Vibrio cholerae isolated from stool. Case-patients were frequency-matched to persons without diarrhea (1:2 by age group, residence), interviewed using standardized questionaires. Logistic regression identified factors associated with case status. Household water was analyzed for fecal coliforms and Escherichia coli. RESULTS: during December 2014-June 2015, 4,218 cholera cases including 282 (6.7%) confirmed cases and 79 deaths (case-fatality rate [CFR] 1.9%) were reported from 14 of 47 Kenyan counties. Nairobi county reported 781 (19.0 %) cases (attack rate, 18/100,000 persons), including 607 (78%) hospitalisations, 20 deaths (CFR 2.6%) and 55 laboratory-confirmed cases (7.0%). Seven (70%) of 10 water samples from communal water points had coliforms; one had Escherichia coli. Factors associated with cholera in Nairobi were drinking untreated water (adjusted odds ratio [aOR] 6.5, 95% confidence interval [CI] 2.3-18.8), lacking health education (aOR 2.4, CI 1.1-7.9) and eating food outside home (aOR 2.4, 95% CI 1.2-5.7). CONCLUSION: we recommend safe water, health education, avoiding eating foods prepared outside home and improved sanitation in Nairobi county. Adherence to these practices could have prevented this protacted cholera outbreak.

Progress towards elimination of measles in Kenya, 2003-2016.

INTRODUCTION: Measles is targeted for elimination in the World Health Organization African Region by the year 2020. In 2011, Kenya was off track in attaining the 2012 pre-elimination goal. We describe the epidemiology of measles in Kenya and assess progress made towards elimination. METHODS: We reviewed national case-based measles surveillance and immunization data from January 2003 to December 2016. A case was confirmed if serum was positive for anti-measles IgM antibody, was epidemiologically linked to a laboratory-confirmed case or clinically compatible. Data on case-patient demographics, vaccination status, and clinical outcome and measles containing vaccine (MCV) coverage were analyzed. We calculated measles surveillance indicators and incidence, using population estimates for the respective years. RESULTS: The coverage of first dose MCV (MCV1) increased from 65% to 86% from 2003-2012, then declined to 75% in 2016. Coverage of second dose MCV (MCV2) remained < 50% since introduction in 2013. During 2003-2016, there were 26,188 suspected measles cases were reported, with 9043(35%) confirmed cases, and 165 deaths (case fatality rate, 1.8%). The non-measles febrile rash illness rate was consistently > 2/100,000 population, and "80% of the sub-national level investigated a case in 11 of the 14 years. National incidence ranged from 4 to 62/million in 2003-2006 and decreased to 3/million in 2016. The age specific incidence ranged from 1 to 364/million population and was highest among children aged < 1 year. CONCLUSION: Kenya has made progress towards measles elimination. However, this progress remains at risk and the recent declines in MCV1 coverage and the low uptake in MCV2 could reverse these gains.

Human and entomologic investigations of chikungunya outbreak in Mandera, Northeastern Kenya, 2016.

Chikungunya is a reemerging vector borne pathogen associated with severe morbidity in affected populations. Lamu, along the Kenyan coast was affected by a major chikungunya outbreak in 2004. Twelve years later, we report on entomologic investigations and laboratory confirmed chikungunya cases in northeastern Kenya. Patient blood samples were received at the Kenya Medical Research Institute (KEMRI) viral hemorrhagic fever laboratory and the immunoglobulin M enzyme linked immunosorbent assay (IgM ELISA) was used to test for the presence of IgM antibodies against chikungunya and dengue. Reverse transcription polymerase chain reaction (RT-PCR) utilizing flavivirus, alphavirus and chikungunya specific primers were used to detect acute infections and representative PCR positive samples sequenced to confirm the circulating strain. Immature mosquitoes were collected from water-holding containers indoors and outdoors in the affected areas in northeastern Kenya. A total of 189 human samples were tested; 126 from Kenya and 63 from Somalia. 52.9% (100/189) tested positive for Chikungunya virus (CHIKV) by either IgM ELISA or RT-PCR. Sequence analysis of selected samples revealed that the virus was closely related to that from China (2010). 29% (55/189) of the samples, almost all from northeastern Kenya or with a history of travel to northern Kenya, tested positive for dengue IgM antibodies. Entomologic risk assessment revealed high house, container and Breteau indices of, 14.5, 41.9 and 17.1% respectively. Underground water storage tanks were the most abundant, 30.1%, of which 77.4% were infested with Aedes aegypti mosquitoes. These findings confirm the presence of active chikungunya infections in the northeastern parts of Kenya. The detection of dengue IgM antibodies concurrently with chikungunya virus circulation emphasizes on the need for improved surveillance systems and diagnostic algorithms with the capacity to capture multiple causes of arbovirus infections as these two viruses share common vectors and eco-systems. In addition sustained entomological surveillance and vector control programs targeting most productive containers are needed to monitor changes in vector densities, for early detection of the viruses and initiate vector control efforts to prevent possible outbreaks.

Interaction between climatic, environmental, and demographic factors on cholera outbreaks in Kenya.

BACKGROUND: Cholera remains an important public health concern in developing countries including Kenya where 11,769 cases and 274 deaths were reported in 2009 according to the World Health Organization (WHO). This ecological study investigates the impact of various climatic, environmental, and demographic variables on the spatial distribution of cholera cases in Kenya. METHODS: District-level data was gathered from Kenya's Division of Disease Surveillance and Response, the Meteorological Department, and the National Bureau of Statistics. The data included the entire population of Kenya from 1999 to 2009. RESULTS: Multivariate analyses showed that districts had an increased risk of cholera outbreaks when a greater proportion of the population lived more than five kilometers from a health facility (RR: 1.025 per 1% increase; 95% CI: 1.010, 1.039), bordered a body of water (RR: 5.5; 95% CI: 2.472, 12.404), experienced increased rainfall from October to December (RR: 1.003 per 1 mm increase; 95% CI: 1.001, 1.005), and experienced decreased rainfall from April to June (RR: 0.996 per 1 mm increase; 95% CI: 0.992, 0.999). There was no detectable association between cholera and population density, poverty, availability of piped water, waste disposal methods, rainfall from January to March, or rainfall from July to September. CONCLUSION: Bordering a large body of water, lack of health facilities nearby, and changes in rainfall were significantly associated with an increased risk of cholera in Kenya.