KnowHub Repository

KnowHub is a corporate knowledge base that provides the system to identify, capture and publish the most critical organizational knowledge products, both tacit and explicit.

Explore Collections on KnowHub

Multimedia

73 Items

View

Publications

1126 Items

View

Reports

67 Items

View

Working Documents

227 Items

View

Communities in Knowhub

Select a community to browse its collections.

Now showing 1 - 4 of 4

Recent Submissions

Access status: Metadata only ,
Bridging the Gap between Public Health, Academia and Policy.
(BMJ, 2026) Christen,P.; Dawa,J.; Ahmed,O.; Akinyi,F.; Masakhwe,B.A.; Bekalo,D.B.; Biwot,C.; Bosire,A.B.; Charles,G.; Gathoni,V.; Gaythorpe,K.A.M.; Geoffrey,G.; Gowa,S.; Hamumy,F.S.; Kagiri,H.; Kamundia,G.; Kamunya,S.; Kariuki,P.; Khalayi,L.; Kombe,I.; Kwamboka,P.; Liétar,P.; Likalamu,N.; Lugonzo,B.; Mariita,V.; Masinde,A.; Mbithi,M.; McCabe,R.; Monari,F.N.; Motiri,F.O.; Muchiri,A.M.; Mudamba,A.; Mugo,P.W.; Mutono,N.; Muriithi,M.; Musasia,R.; Mutunga,M.; Mwangi,W.; Nalwa,J.; Ngeno,K.I.; Nyaberi,J.M.; Nzoka,P.M.; Ojal,J.; Okech,T.; Okunga,E.; Omondi,E.; Osoro,J.; Osoro,P.; Patel,A.; Sayianka,S.; Shigoli,T.; Silali,C.; Thendu,M.M.; vanElsland,S.L.; Wanjiku,K.A.; Muchemi,P.; Whittles,L.K.; Winskill,P.; Ombajo,L.A.; Thumbi,S.M.; Watson,O.J.
The use of advanced analytics in public health policy remains hindered by a disconnect between researchers, policymakers and technical experts. Bridging this gap requires intentional knowledge translation strategies that facilitate interdisciplinary collaboration and real-world application of research findings. Hackathons, which bring together diverse stakeholders in a time-bound, solution-oriented format, offer an approach to address this challenge. In January 2025, the MRC Centre for Global Infectious Disease Analysis and the Centre for Epidemiological Modelling and Analysis at the University of Nairobi organised the Bridging the Gap Hackathon, designed to strengthen collaboration between academia, policy and public health practitioners in Kenya. The hackathon convened researchers, software engineers and policymakers to co-develop data-driven tools to tackle public health challenges identified by Kenya's Ministry of Health and the Directorate of Veterinary Services. Over five?days and using a structured multi-stage process, six interdisciplinary teams developed prototype solutions to improve outbreak surveillance, vaccine deployment, data quality monitoring and health workforce estimation. This paper reflects on the hackathon's structure, participant experiences and project outcomes, highlighting key lessons for future knowledge translation initiatives. Our findings suggest that hackathons can serve as effective platforms for accelerating interdisciplinary research impact, fostering engagement between policymakers and researchers and promoting the development of solutions to public health issues.
Access status: Metadata only ,
Evaluating the Impact of OMOP-CDM on Data Quality Insight Generation in Respiratory Disease Management.
(Frontiers, 2026) Yankam,B.M.; Luc Baudoin,F.T.; Andeso,P.; Onana Akoa,F.A.; Ebimbe,J.B.; Barasa,M.; Onana,M.; Iddi,S.; Kiragga,A.; Mbatchou Ngahane,B.H; Data Science Without Borders Project
The increasing volume and heterogeneity of patient care data present significant challenges for comprehensive analysis and the generation of insights, particularly in specific areas such as respiratory diseases. Standardizing diverse health data is crucial for enabling large-scale observational research and ensuring data readiness. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a widely adopted standard for harmonizing such data. However, evaluating the quality of data transformed into the OMOP CDM format is a critical step before its use in research or clinical decision support. This study evaluates the impact of the OMOP CDM standardization process on generating data quality insights for a respiratory disease dataset. The source dataset was initially paper-based, converted to an electronic format, and translated from French into English. This historical dataset covers the years 2009-2023 and contains 108 variables and 2,154 records. The data underwent the standard Extract, Transform, and Load (ETL) process to convert into the OMOP CDM format. Following this transformation, the quality of the resulting OMOP CDM instance was assessed. We utilized the Data Quality Dashboard (DQD) to evaluate the quality of the OMOP CDM database before and after ETL verification. DQD performs validation checks on the data based on key data quality dimensions, including completeness, plausibility, and conformance. Overall, the assessment conducted 2,344 checks, of which 2,269 passed, and 75 failed, resulting in a corrected pass rate of 96% for the Respiratory Diseases Inpatients data before ETL verification. After ETL verification, the assessment conducted 2,374 checks, of which 2,356 passed, and 40 failed, resulting in a 100% corrected pass rate. Standardizing respiratory disease data using the OMOP CDM enabled a structured and transparent evaluation of data quality. Through the application of the DQD, this study demonstrated the utility of OMOP CDM in generating meaningful data quality insights. These findings highlight the model's potential to enhance data readiness and support evidence-based decision-making in respiratory disease management.
Access status: Metadata only ,
Food Purchase Patterns in Nairobi Before, During, and After The COVID-19 Pandemic Lockdown Measures.
(Taylor & Francis, 2026) Momanyi,R.; Kavu,T.D.; Mwanga,D.M.; Karugu,C.H.; Cygu,S.; Asiki,G.; Kiragga,A.
The nationwide lockdown measures implemented during the coronavirus disease 2019 (COVID-19) pandemic disrupted food supply systems, potentially altering consumer purchasing behaviour. There is limited evidence quantifying these changes in low- and middle-income settings. This study aimed to examine the impact of COVID-19 on grocery purchase patterns among consumers in Nairobi. Using generalized least squares (GLS), we conducted an interrupted time series (ITS) analysis of weekly food purchase data from 2018 to 2023. The analysis considered three periods: pre-COVID (12th January 2018-26th March 2020), COVID (27th March 2020-21st October 2021), and post-COVID (22nd October 2021-31st December 2023). A total of 11,105,974 transactions from two supermarkets in Nairobi were classified using the NOVA food classification and linked with nutrient composition data. Compared to the pre-COVID period, characterized by declining purchases of processed culinary, unprocessed/minimally processed foods, and increasing ultra-processed food (UPF) purchases, the COVID period was associated with a short-term change toward healthier purchasing patterns, including reduced UPF, with increased processed and unprocessed/minimally processed food purchases. Nutritionally, pre-COVID trends of rising energy and carbohydrate purchases and declining proteins, calcium, iron, magnesium, potassium, and sodium, were contrasted by short-term increases during COVID in fibre, iron, magnesium, phosphorus, potassium, and sodium, alongside a long-term decline in carbohydrates. Proteins showed consistent short- and long-term increases, while calcium rose sharply at COVID onset but declined over time. In comparison, the post-COVID period reflected a reversal of these changes. While processed food purchases increased briefly before declining, longer-term trends showed increases in calcium, sodium, and carbohydrate purchases and decreases in energy and fat. These findings shed light on how populations adapt their food purchasing behaviors during and after global crises, offering insights that can inform future policies aimed at curbing unhealthy food purchases and strengthening food security.
Access status: Metadata only ,
Compound Hot-Dry Extremes in Senegal: Trends and Time of Emergence in a Warming World
(JRC Publications, 2026) Seck,A.; Sylla,M.B.; Faye,A.; Dosio,A.; Monerie,P.-A.; Gaye,A.T.; Moustapha,T.
This study examines the duration, intensity, and frequency of compound hot and dry extremes (CHDEs) in Senegal, together with their time of emergence (ToE), using daily temperature and precipitation from ten NASA Earth Exchange Global Daily Downscaled Projections-Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) models under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Observations from the Climate Hazards Group Infrared Temperature with Stations dataset for temperature and the Climate Hazards Group InfraRed Precipitation with Stations dataset for precipitation are used for historical evaluation (1985-2014). During this period, CHDE duration reaches up to 4 d in northern Senegal, with maximum intensities of 44 °C–46 °C and frequencies of 3-4 events per year. A slight upward trend in duration is observed, consistent with accelerated warming since the 1980s. The multi-model ensemble reproduces spatial and temporal CHDE patterns reasonably well, though duration and intensity are underestimated in northern Senegal and frequency is overestimated in the northwest coastal zone. Projections indicate substantial increases across all scenarios, with the largest changes under SSP5-8.5, where duration may lengthen by over six days, intensity may exceed historical baseline by 8 °C, and frequency may reach 10-14 events by 2100. The ToE analysis shows that under SSP5-8.5, CHDE intensity and frequency may emerge from historical variability as early as 2025, much earlier than under SSP1-2.6. The early and widespread emergence of CHDEs, particularly in the northern and coastal regions, emphasises the urgent need for robust adaptation strategies and rapid emission reductions. These findings offer crucial insights for climate risk management and can inform national planning efforts to mitigate the escalating threats of compound climate extremes.
Access status: Metadata only ,
Characterization of Clinical Diagnoses at a Community Telehealth Platform in Uganda.
(Sage Journal, 2026) Otieno,F.; Mwanga,D.; Mawanda,A.; Kamulegeya,L.; Bwanika,J.M.; Kadengye,D.; Kiragga,A.
Rocket Health Africa (formerly The Medical Concierge Group) pioneered telehealth in Uganda by integrating technology with personalized care, providing consultations, diagnostics, pharmacy services, laboratory services, clinics, and vaccination services on a single platform. This study examined the diagnostic and demographic profiles captured through Rocket Health's digital health platform from 2019 to 2023, across the prepandemic, pandemic, and postpandemic periods, using descriptive statistics. A total of 2,677 users were recorded on the telehealth platform during the prepandemic period, 47,072 during the pandemic, and 99,904 during the postpandemic period. Male users accounted for the largest proportion of platform users across all three periods. Married individuals constituted the largest proportion by marital status categories, while users aged 25-34 accounted for the highest proportion within the age group distribution. A range of diagnostic categories was documented across the study period. Diseases of the respiratory system, such as acute upper respiratory infection, were the most frequently recorded diagnostic category, followed by symptoms, signs, and abnormal clinical and laboratory findings not elsewhere classified, including fever, headache, and cough. Certain infectious and parasitic diseases, such as coronavirus infection, candidiasis of the vulva and vagina, and Plasmodium falciparum malaria, constituted the third most frequently recorded diagnostic category. Telehealth utilization in Uganda was documented across diverse demographic groups and diagnostic categories over three distinct periods. These findings inform future telehealth policy and planning in Uganda and sub-Saharan Africa.