Experience in fostering regional collaboration and Coordination to use data for battling infectious diseases in sub-Saharan Africa.

The emergence of COVID-19 highlights globalisation realties, where diseases may emerge from anywhere and rapidly spread globally. Lessons emphasise the necessity for strengthening regional and global collaboration and coordination to allow rapid risk identification, resource mobilisation and joint actions. We report the experience of the Regional Action through Data (RAD) partnership in fostering regional cooperation and collaboration to use data for battling infectious diseases and the effects of COVID-19. The Partnership comprised;BoadReach company, The West African Health Organization (WAHO) and the Intergovernmental Authority on Development (IGAD); Duke University Global Health Centre and the Jembi Health Systems, South Africa. MAIN OBJECTIVE: To address the problem of limited used of data to drive performance in healthcare service delivery in sub-Saharan Africa; by changing how and why data is collected, analysed, and then used to achieve results. SPECIFIC OBJECTIVES: 1. Regional level: To equip and empower IGAD and WAHO with evidence-based analytics to drive data use for evidence-based policy and program action in public health (regional level). 2. Patient-provider level: To deploy and implement a digital health solution for child-hood vaccination services focused on mobile cross-border populations along the Uganda-Kenya border.Engagement approaches used included; meetings, workshops, technical working groups, establishing monitoring system and annual implementation revision. Targeted training and capacity building were conducted. All activities were built on existing systems and structures to strengthen ownership and sustainability. REGIONAL LEVEL ACHIEVEMENTS: 1. Regional health data sharing and protection policy, 2. Strengthened regional health information platform. Patient provider level: Deployment of a cloud based digital health solution to enhance childhood access to vaccination services for cross border populations of Kenya and Uganda, 3. Both regions developed resource mobilisation plans for sustainability.RAD established the foundation for building trust and strengthening regional collaboration and coordination in health in Sub-Saharan Africa.

Identification and characterization of influenza A viruses in selected domestic animals in Kenya, 2010-2012.

BACKGROUND: Influenza A virus subtypes in non-human hosts have not been characterized in Kenya. We carried out influenza surveillance in selected domestic animals and compared the virus isolates with isolates obtained in humans during the same period. METHODS: We collected nasal swabs from pigs, dogs and cats; oropharyngeal and cloacal swabs from poultry; and blood samples from all animals between 2010 and 2012. A standardized questionnaire was administered to farmers and traders. Swabs were tested for influenza A by rtRT-PCR, virus isolation and subtyping was done on all positive swabs. All sera were screened for influenza A antibodies by ELISA, and positives were evaluated by hemagglutination inhibition (HI). Full genome sequencing was done on four selected pig virus isolates. RESULTS: Among 3,798 sera tested by ELISA, influenza A seroprevalence was highest in pigs (15.9%; 172/1084), 1.2% (3/258) in ducks, 1.4% (1/72) in cats 0.6% (3/467) in dogs, 0.1% (2/1894) in chicken and 0% in geese and turkeys. HI testing of ELISA-positive pig sera showed that 71.5% had positive titers to A/California/04/2009(H1N1). Among 6,289 swabs tested by rRT-PCR, influenza A prevalence was highest in ducks [1.2%; 5/423] and 0% in cats and turkeys. Eight virus isolates were obtained from pig nasal swabs collected in 2011 and were determined to be A(H1N1)pdm09 on subtyping. On phylogenetic analysis, four hemagglutinin segments from pig isolates clustered together and were closely associated with human influenza viruses that circulated in Kenya in 2011. CONCLUSION: Influenza A(H1N1)pdm09 isolated in pigs was genetically similar to contemporary human pandemic influenza virus isolates. This suggest that the virus was likely transmitted from humans to pigs, became established and circulated in Kenyan pig populations during the study period. Minimal influenza A prevalence was observed in the other animals studied.

Which influenza vaccine formulation should be used in Kenya? A comparison of influenza isolates from Kenya to vaccine strains, 2007-2013.

INTRODUCTION: Every year the World Health Organization (WHO) recommends which influenza virus strains should be included in a northern hemisphere (NH) and a southern hemisphere (SH) influenza vaccine. To determine the best vaccine formulation for Kenya, we compared influenza viruses collected in Kenya from April 2007 to May 2013 to WHO vaccine strains. METHODS: We collected nasopharyngeal and oropharyngeal (NP/OP) specimens from patients with respiratory illness, tested them for influenza, isolated influenza viruses from a proportion of positive specimens, tested the isolates for antigenic relatedness to vaccine strains, and determined the percentage match between circulating viruses and SH or NH influenza vaccine composition and schedule. RESULTS: During the six years, 7.336 of the 60,072 (12.2%) NP/OP specimens we collected were positive for influenza: 30,167 specimens were collected during the SH seasons and 3717 (12.3%) were positive for influenza; 2903 (78.1%) influenza A, 902 (24.2%) influenza B, and 88 (2.4%) influenza A and B positive specimens. We collected 30,131 specimens during the NH seasons and 3978 (13.2%) were positive for influenza; 3181 (80.0%) influenza A, 851 (21.4%) influenza B, and 54 (1.4%) influenza A and B positive specimens. Overall, 362/460 (78.7%) isolates from the SH seasons and 316/338 (93.5%) isolates from the NH seasons were matched to the SH and the NH vaccine strains, respectively (p<0.001). Overall, 53.6% and 46.4% SH and NH vaccines, respectively, matched circulating strains in terms of vaccine strains and timing. CONCLUSION: In six years of surveillance in Kenya, influenza circulated at nearly equal levels during the SH and the NH influenza seasons. Circulating viruses were matched to vaccine strains. The vaccine match decreased when both vaccine strains and timing were taken into consideration. Either vaccine formulation could be suitable for use in Kenya but the optimal timing for influenza vaccination needs to be determined.

The Role of HIV in the Household Introduction and Transmission of Influenza in an Urban Slum, Nairobi, Kenya, 2008-2011.

BACKGROUND: Little is known about how human immunodeficiency virus (HIV) infection affects influenza transmission within homes in sub-Saharan Africa. METHODS: We used respiratory illness surveillance and HIV testing data gathered in Kibera, an urban slum in Nairobi, Kenya, to examine the impact of HIV status on (1) introducing influenza to the home and (2) transmitting influenza to household contacts. RESULTS: While HIV status did not affect the likelihood of being an influenza index case, household contacts of HIV-infected influenza index cases had twice the risk of developing secondary influenza-like illness than contacts of HIV-negative index cases. CONCLUSIONS: HIV-infected influenza index cases may facilitate transmission of influenza within the home.

A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011-2012.

BACKGROUND: For disease surveillance, manual data collection using paper-based questionnaires can be time consuming and prone to errors. We introduced smartphone data collection to replace paper-based data collection for an influenza sentinel surveillance system in four hospitals in Kenya. We compared the quality, cost and timeliness of data collection between the smartphone data collection system and the paper-based system. METHODS: Since 2006, the Kenya Ministry of Health (MoH) with technical support from the Kenya Medical Research Institute/Centers for Disease Control and Prevention (KEMRI/CDC) conducted hospital-based sentinel surveillance for influenza in Kenya. In May 2011, the MOH replaced paper-based collection with an electronic data collection system using Field Adapted Survey Toolkit (FAST) on HTC Touch Pro2 smartphones at four sentinel sites. We compared 880 paper-based questionnaires dated Jan 2010-Jun 2011 and 880 smartphone questionnaires dated May 2011-Jun 2012 from the four surveillance sites. For each site, we compared the quality, cost and timeliness of each data collection system. RESULTS: Incomplete records were more likely seen in data collected using pen-and-paper compared to data collected using smartphones (adjusted incidence rate ratio (aIRR) 7, 95% CI: 4.4-10.3). Errors and inconsistent answers were also more likely to be seen in data collected using pen-and-paper compared to data collected using smartphones (aIRR: 25, 95% CI: 12.5-51.8). Smartphone data was uploaded into the database in a median time of 7 days while paper-based data took a median of 21 days to be entered (p < 0.01). It cost USD 1,501 (9.4%) more to establish the smartphone data collection system ($17,500) than the pen-and-paper system (USD $15,999). During two years, however, the smartphone data collection system was $3,801 (7%) less expensive to operate ($50,200) when compared to pen-and-paper system ($54,001). CONCLUSIONS: Compared to paper-based data collection, an electronic data collection system produced fewer incomplete data, fewer errors and inconsistent responses and delivered data faster. Although start-up costs were higher, the overall costs of establishing and running the electronic data collection system were lower compared to paper-based data collection system. Electronic data collection using smartphones has potential to improve timeliness, data integrity and reduce costs.

Uptake and effectiveness of monovalent influenza A (H1N1) pandemic 2009 vaccine among healthcare personnel in Kenya, 2010.

INTRODUCTION: During April-June 2010, the Kenya Ministry of Public Health and Sanitation distributed free monovalent influenza A(H1N1)pdm09 vaccines to health care personnel (HCP) and other vulnerable groups. We conducted a prospective, cohort study among HCP to characterize influenza A(H1N1)pdm09 vaccine uptake, and to assess influenza A(H1N1)pdm09 vaccine effectiveness. METHODS: We enrolled HCP from 5 hospitals and followed them for 6 months. At enrollment, we asked HCP if they had received the influenza A(H1N1)pdm09 vaccine and their reasons for their decision. We administered weekly questionnaires to participants about respiratory symptoms suffered during the previous week. Participants who had acute respiratory illness were asked to contact our surveillance clinician, and nasopharyngeal and oropharyngeal specimens were collected and later tested for influenza by real-time reverse-transcriptase polymerase-chain-reaction. Vaccine effectiveness was estimated by comparing the incidence of acute respiratory illness, absenteeism from work due to respiratory illness and laboratory-confirmed influenza among vaccinated and unvaccinated HCP. RESULTS: We enrolled 3803 HCP from the five hospitals; 64% received influenza vaccine. Vaccinated HCP were more likely to develop acute respiratory illness (ARI) and more likely to report missed days of work due to respiratory illness compared to non-vaccinated HCP (adjusted incidence rate ratio (aIRR) 1.50, 95% confidence intervals (CI): 1.33-1.70) and (aIRR 2.02, 95% CI: 1.41-2.88), respectively. Of 531 samples collected from vaccinated and non-vaccinated HCP, 30 were influenza A and 3 were influenza B. Two influenza A(H1N1)pdm09 subtypes were isolated; one from vaccinated and the other from non-vaccinated HCP. DISCUSSION AND CONCLUSIONS: A majority of Kenyan HCP surveyed reported receiving the influenza A(H1N1)pdm09 vaccine. Because of low circulation of influenza A(H1N1)pdm09 virus during the study period, vaccine effectiveness could not be determined. The findings of increased ARI events and missed days of work among vaccinated HCP were likely confounded by vaccine-seeking behavioral factors.

Assessing parents' knowledge and attitudes towards seasonal influenza vaccination of children before and after a seasonal influenza vaccination effectiveness study in low-income urban and rural Kenya, 2010-2011.

BACKGROUND: Influenza vaccine is rarely used in Kenya, and little is known about attitudes towards the vaccine. From June-September 2010, free seasonal influenza vaccine was offered to children between 6 months and 10 years old in two Population-Based Infectious Disease Surveillance (PBIDS) sites. This survey assessed attitudes about influenza, uptake of the vaccine and experiences with childhood influenza vaccination. METHODS: We administered a questionnaire and held focus group discussions with parents of children of enrollment age in the two sites before and after first year of the vaccine campaign. For pre-vaccination focus group discussions, we randomly selected mothers and fathers who had an eligible child from the PBIDS database to participate. For the post-vaccination focus group discussions we stratified parents whose children were eligible for vaccination into fully vaccinated, partially vaccinated and non-vaccinated groups. RESULTS: Overall, 5284 and 5755 people completed pre and post-vaccination questionnaires, respectively, in Kibera and Lwak. From pre-vaccination questionnaire results, among parents who were planning on vaccinating their children, 2219 (77.6%) in Kibera and 1780 (89.6%) in Lwak said the main reason was to protect the children from seasonal influenza. In the pre-vaccination discussions, no parent had heard of the seasonal influenza vaccine. At the end of the vaccine campaign, of 18,652 eligible children, 5,817 (31.2%) were fully vaccinated, 2,073 (11.1%) were partially vaccinated and, 10,762 (57.7%) were not vaccinated. In focus group discussions, parents who declined vaccine were concerned about vaccine safety or believed seasonal influenza illness was not severe enough to warrant vaccination. Parents who declined the vaccine were mainly too busy [251(25%) in Kibera and 95 (10.5%) in Lwak], or their child was away during the vaccination period [199(19.8%) in Kibera; 94(10.4%) in Lwak]. CONCLUSION: If influenza vaccine were to be introduced more broadly in Kenya, effective health messaging will be needed on vaccine side effects and frequency and potential severity of influenza infection.

Detection of influenza A virus in live bird markets in Kenya, 2009-2011.

BACKGROUND: Surveillance for influenza viruses within live bird markets (LBMs) has been recognized as an effective tool for detecting circulating avian influenza viruses (AIVs). In Sub-Saharan Africa, limited data exist on AIVs in animal hosts, and in Kenya the presence of influenza virus in animal hosts has not been described. OBJECTIVES: This surveillance project aimed to detect influenza A virus in poultry traded in five LBMs in Kenya. METHODS: We visited each market monthly and collected oropharyngeal and cloacal specimens from poultry and environmental specimens for virological testing for influenza A by real time RT-PCR. On each visit, we collected information on the number and types of birds in each market, health status of the birds, and market practices. RESULTS: During March 24, 2009-February 28, 2011, we collected 5221 cloacal and oropharyngeal swabs. Of the 5199 (99·6%) specimens tested, influenza A virus was detected in 42 (0·8%), including 35/4166 (0·8%) specimens from chickens, 3/381 (0·8%) from turkeys, and 4/335 (1·2%) from geese. None of the 317 duck specimens were positive. Influenza was more commonly detected in oropharyngeal [33 (1·3%)] than in cloacal [9 (0·4%)] specimens. None of the 485 environmental specimens were positive. Virus was detected in all five markets during most (14/22) of the months. Ducks and geese were kept longer at the market (median 30 days) than chickens (median 2days). CONCLUSIONS: Influenza A was detected in a small percentage of poultry traded in LBMs in Kenya. Efforts should be made to promote practices that could limit the maintenance and transmission of AIVs in LBMs.